%0 Journal Article
%J IEEE Transactions on Evolutionary Computation
%D 2016
%T Seeking Multiple Solutions: an Updated Survey on Niching Methods and Their Applications
%A X. Li
%A M. G. Epitropakis
%A K. Deb
%A A. Engelbrecht
%K Benchmark testing
%K evolutionary computation
%K Meta-heuristics
%K Multi-modal optimization
%K Multi-solution methods
%K Niching methods
%K Optimization methods
%K Problem-solving
%K Sociology
%K Statistics
%K Swarm intelligence
%K Two dimensional displays
%X Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.
%B IEEE Transactions on Evolutionary Computation
%V PP
%P 1-1
%G eng
%R 10.1109/TEVC.2016.2638437
%0 Book Section
%B Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings
%D 2016
%T Tutorials at PPSN 2016
%A Doerr, Carola
%A Bredeche, Nicolas
%A Alba, Enrique
%A Bartz-Beielstein, Thomas
%A Brockhoff, Dimo
%A Doerr, Benjamin
%A Eiben, Gusz
%A Epitropakis, Michael G.
%A Fonseca, Carlos M.
%A Guerreiro, Andreia
%A Haasdijk, Evert
%A Heinerman, Jacqueline
%A Hubert, Julien
%A Lehre, Per Kristian
%A Malagò, Luigi
%A Merelo, J. J.
%A Miller, Julian
%A Naujoks, Boris
%A Oliveto, Pietro
%A Picek, Stjepan
%A Pillay, Nelishia
%A Preuss, Mike
%A Ryser-Welch, Patricia
%A Squillero, Giovanni
%A Stork, Jörg
%A Sudholt, Dirk
%A Tonda, Alberto
%A Whitley, Darrell
%A Zaefferer, Martin
%E Handl, Julia
%E Hart, Emma
%E Lewis, Peter R.
%E López-Ibáñez, Manuel
%E Ochoa, Gabriela
%E Paechter, Ben
%X PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!
%B Parallel Problem Solving from Nature – PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings
%I Springer International Publishing
%C Cham
%P 1012–1022
%@ 978-3-319-45823-6
%G eng
%U http://dx.doi.org/10.1007/978-3-319-45823-6_95
%R 10.1007/978-3-319-45823-6_95
%0 Conference Paper
%B International Symposium on Software Testing and Analysis (ISSTA'15)
%D 2015
%T Empirical Evaluation of Pareto Efficient Multi-objective Regression Test Case Prioritisation
%A Michael G. Epitropakis
%A Shin Yoo
%A Mark Harman
%A Edmund K. Burke
%K additional greedy algorithm
%K coverage compaction
%K multi-objective evolutionary algo- rithm
%K Test case prioritization
%X The aim of test case prioritisation is to determine an ordering of test cases that maximises the likelihood of early fault revelation. Previous prioritisation techniques have tended to be single objective, for which the additional greedy algorithm is the current state-of-the-art. Unlike test suite minimisation, multi objective test case prioritisation has not been thoroughly evaluated. This paper presents an extensive empirical study of the effectiveness of multi objective test case prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1 million lines of code. The paper also presents a lossless coverage compaction algorithm that dramatically scales the performance of all algorithms studied by between 2 and 4 orders of magnitude, making prioritisation practical for even very demanding problems.
%B International Symposium on Software Testing and Analysis (ISSTA'15)
%I ACM
%C Baltimore, MD, USA
%G eng
%0 Report
%D 2014
%T Gen-O-Fix: An embeddable framework for Dynamic Adaptive Genetic Improvement Programming
%A Jerry Swan
%A Michael G. Epitropakis
%A John R. Woodward
%X Genetic Improvement Programming (GIP) is concerned with automating the burden of software maintenance, the most costly phase of the software lifecycle. We describe Gen-O-Fix, a GIP framework which allows a software system hosted on the Java Virtual Machine to be continually improved (e.g. make better predictions; pass more regression tests; reduce power consumption). It is the first exemplar of a dynamic adaptive GIP framework, i.e. it can improve a system as it runs. It is written in the Scala programming language and uses reflection to yield source-to-source transformation. One of the design goals for Gen-O-Fix was to create a tool that is user-centric rather than researcher-centric: the end-user is required only to provide a measure of system quality and the URL of the source code to be improved. We discuss potential applications to predictive, embedded and high-performance systems.
%I Computing Science and Mathematics, University of Stirling
%C Stirling FK9 4LA, Scotland
%P 1-12
%8 01/2014
%G eng
%0 Report
%D 2014
%T Pareto Efficient Multi-Objective Regression Test Suite Prioritisation
%A Michael G. Epitropakis
%A Shin Yoo
%A Mark Harman
%A Edmund K. Burke
%X Test suite prioritisation seeks a test case ordering that maximises the likelihood of early fault revelation. Previous prioritisation techniques have tended to be single objective, for which the additional greedy algorithm is the current state-of-the-art. We study multi objective test suite prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1MLoC. Our multi objective algorithms find faults significantly faster and with large effect size for 20 of the 22 versions. We also introduce a non-lossy coverage compact algorithm that dramatically scales the performance of all algorithms studied by between 2 and 4 orders of magnitude, making prioritisation practical for even very demanding problems.
%I Department of Computer Science, University College London
%C Gower Street, London
%P 1--16
%8 04/2014
%G eng
%0 Book Section
%B Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings
%D 2014
%T Repairing and Optimizing Hadoop hashCode Implementations
%A Kocsis, Zoltan A.
%A Neumann, Geoff
%A Swan, Jerry
%A Epitropakis, Michael G.
%A Brownlee, Alexander E. I.
%A Haraldsson, Sami O.
%A Bowles, Edward
%E Le Goues, Claire
%E Yoo, Shin
%X We describe how contract violations in Java TM hashCode methods can be repaired using novel combination of semantics-preserving and generative methods, the latter being achieved via Automatic Improvement Programming. The method described is universally applicable. When applied to the Hadoop platform, it was established that it produces hashCode functions that are at least as good as the original, broken method as well as those produced by a widely-used alternative method from the ‘Apache Commons’ library.
%B Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings
%I Springer International Publishing
%C Cham
%P 259–264
%@ 978-3-319-09940-8
%G eng
%U http://dx.doi.org/10.1007/978-3-319-09940-8_22
%R 10.1007/978-3-319-09940-8_22
%0 Conference Paper
%B Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on
%D 2014
%T A Separability Prototype for Automatic Memes with Adaptive Operator Selection
%A Epitropakis, M.G.
%A Caraffini, F.
%A Neri, F.
%A Burke, E.K.
%K Adaptation models
%K adaptive model
%K adaptive operator selection
%K Algorithm design and analysis
%K algorithmics
%K automatic design
%K Benchmark testing
%K hyper-heuristics
%K memetic computing
%K optimisation
%K optimization
%K optimization problems
%K Prototypes
%K search algorithms
%K search problems
%K search process
%K separability prototype for automatic memes
%K Software algorithms
%K software prototype
%K software prototyping
%K SPAM-AOS
%K Unsolicited electronic mail
%X One of the main challenges in algorithmics in general, and in Memetic Computing, in particular, is the automatic design of search algorithms. A recent advance in this direction (in terms of continuous problems) is the development of a software prototype that builds up an algorithm based upon a problem analysis of its separability. This prototype has been called the Separability Prototype for Automatic Memes (SPAM). This article modifies the SPAM by incorporating within it an adaptive model used in hyper-heuristics for tackling optimization problems. This model, namely Adaptive Operator Selection (AOS), rewards at run time the most promising heuristics/memes so that they are more likely to be used in the following stages of the search process. The resulting framework, here referred to as SPAM-AOS, has been tested on various benchmark problems and compared with modern algorithms representing the-state-of-the-art of search for continuous problems. Numerical results show that the proposed SPAM-AOS is a promising framework that outperforms the original SPAM and other modern algorithms. Most importantly, this study shows how certain areas of Memetic Computing and Hyper-heuristics are very closely related topics and it also shows that their combination can lead to the development of powerful algorithmic frameworks.
%B Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on
%8 Dec
%G eng
%R 10.1109/FOCI.2014.7007809
%0 Report
%D 2013
%T Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'
%A Xiaodong Li
%A Andries Engelbrecht
%A M. G. Epitropakis
%I Evolutionary Computation and Machine Learning Group, RMIT University
%C Melbourne, Australia
%G eng
%U http://goanna.cs.rmit.edu.au/~xiaodong/cec13-niching/competition/
%0 Journal Article
%J International Journal of Bifurcation and Chaos (IJBC)
%D 2013
%T Chaos and Music: From time series analysis to evolutionary composition
%A M. A. Kaliakatsos-Papakostas
%A M. G. Epitropakis
%A A. Floros
%A M. N. Vrahatis
%X Music is an amalgam of logic and emotion, order and dissonance, along with many combinations of contradicting notions which allude to deterministic chaos. Therefore, it comes as no surprise that several research works have examined the utilization of dynamical systems for symbolic music composition. The main motivation of the paper at hand is the analysis of the tonal composition potentialities of several discrete dynamical systems, in comparison to genuine human compositions. Therefore, a set of human musical compositions is utilized to provide ``compositional guidelines'' to several dynamical systems, the parameters of which are properly adjusted through evolutionary computation. This procedure exposes the extent to which a system is capable of composing tonal sequences that resemble human composition. In parallel, a time series analysis on the genuine compositions is performed, which firstly provides an overview of their dynamical characteristics and secondly, allows a comparative analysis with the dynamics of the artificial compositions. The results expose the tonal composition capabilities of the examined iterative maps, providing specific references to the tonal characteristics that they can capture.
%B International Journal of Bifurcation and Chaos (IJBC)
%V 23
%P 1350181
%G eng
%U http://www.worldscientific.com/doi/abs/10.1142/S0218127413501812
%N 11
%R 10.1142/S0218127413501812
%0 Conference Proceedings
%B IEEE Congress on Evolutionary Computation, 2013. CEC 2013
%D 2013
%T A Dynamic Archive Niching Differential Evolution Algorithm for Multimodal Optimization
%A M. G. Epitropakis
%A Xiaodong Li
%A Edmund K. Burke
%X Highly multimodal landscapes with multiple local/global optima represent common characteristics in real-world applications. Many niching algorithms have been proposed in the literature which aim to search such landscapes in an attempt to locate as many global optima as possible. However, to locate and maintain a large number of global solutions, these algorithms are substantially influenced by their parameter values, such as a large population size. Here, we propose a new niching Differential Evolution algorithm that attempts to overcome the population size influence and produce good performance almost independently of its population size. To this end, we incorporate two mechanisms into the algorithm: a control parameter adaptation technique and an external dynamic archive along with a reinitialization mechanism. The first mechanism is designed to efficiently adapt the control parameters of the algorithm, whilst the second one is responsible for enabling the algorithm to investigate unexplored regions of the search space and simultaneously keep the best solutions found by the algorithm. The proposed approach is compared with two Differential Evolution variants on a recently proposed benchmark suite. Empirical results indicate that the proposed niching algorithm is competitive and very promising. It exhibits a robust and stable behavior, whilst the incorporation of the dynamic archive seems to tackle the population size influence effectively. Moreover, it alleviates the problem of having to fine-tune the population size parameter in a niching algorithm.
%B IEEE Congress on Evolutionary Computation, 2013. CEC 2013
%C Cancun, Mexico
%P 79-86
%8 June
%G eng
%U http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6557556
%R 10.1109/CEC.2013.6557556
%0 Journal Article
%J Soft Computing - A Fusion of Foundations, Methodologies and Applications
%D 2012
%T Controlling interactive evolution of 8-bit melodies with genetic programming
%A M. A. Kaliakatsos–Papakostas
%A M. G. Epitropakis
%A A. Floros
%A M. N. Vrahatis
%X Automatic music composition and sound synthesis is a field of study that gains continuously increasing attention. The introduction of evolutionary computation has further boosted the research towards exploring ways to incorporate human supervision and guidance in the automatic evolution of melodies and sounds. This kind of human–machine interaction belongs to a larger methodological context called interactive evolution (IE). For the automatic creation of art and especially for music synthesis, user fatigue requires that the evolutionary process produces interesting content that evolves fast. This paper addresses this issue by presenting an IE system that evolves melodies using genetic programming (GP). A modification of the GP operators is proposed that allows the user to have control on the randomness of the evolutionary process. The results obtained by subjective tests indicate that the utilization of the proposed genetic operators drives the evolution to more user-preferable sounds.
%B Soft Computing - A Fusion of Foundations, Methodologies and Applications
%V 16
%P 1997-2008
%G eng
%R 10.1007/s00500-012-0872-y
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)
%D 2012
%T Density Based Projection Pursuit Clustering
%A S. K. Tasoulis
%A M. G. Epitropakis
%A V. P. Plagianakos
%A D. K. Tasoulis
%X Clustering of high dimensional data is a very important task in Data Mining. In dealing with such data, we typically need to use methods like Principal Component Analysis and Projection Pursuit, to find interesting lower dimensional directions to project the data and hence reduce their dimensionality in a manageable size. In this work, we propose a new criterion of direction interestingness, which incorporates information from the density of the projected data. Subsequently, we utilize the Differential Evolution algorithm to perform optimization over the space of the projections and hence construct a new hierarchical clustering algorithmic scheme. The new algorithm shows promising performance over a series of real and simulated data.
%B IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)
%C Brisbane, Australia
%8 June
%G eng
%R 10.1109/CEC.2012.6253006
%0 Journal Article
%J Information Sciences
%D 2012
%T Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%X In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. In this paper, motivated by the behavior and the spatial characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid framework that combines the Particle Swarm Optimization and the Differential Evolution algorithm. Particle Swarm Optimization has the tendency to distribute the best personal positions of the swarm particles near to the vicinity of problem’s optima. In an attempt to efficiently guide the evolution and enhance the convergence, we evolve the personal experience or memory of the particles with the Differential Evolution algorithm, without destroying the search capabilities of the algorithm. The proposed framework can be applied to any Particle Swarm Optimization algorithm with minimal effort. To evaluate the performance and highlight the different aspects of the proposed framework, we initially incorporate six classic Differential Evolution mutation strategies in the canonical Particle Swarm Optimization, while afterwards we employ five state-of-the-art Particle Swarm Optimization variants and four popular Differential Evolution algorithms. Extensive experimental results on 25 high dimensional multimodal benchmark functions along with the corresponding statistical analysis, suggest that the hybrid variants are very promising and significantly improve the original algorithms in the majority of the studied cases.
%B Information Sciences
%V 216
%P 50-92
%G eng
%R 10.1016/j.ins.2012.05.017
%0 Book Section
%B Evolutionary and Biologically Inspired Music, Sound, Art and Design
%D 2012
%T Interactive Evolution of 8–Bit Melodies with Genetic Programming towards Finding Aesthetic Measures for Sound
%A M. A. Kaliakatsos–Papakostas
%A M. G. Epitropakis
%A A. Floros
%A M. N. Vrahatis
%E Penousal Machado
%E Juan Romero
%E Adrian Carballal
%X The efficient specification of aesthetic measures for music as a part of modelling human conception of sound is a challenging task and has motivated several research works. It is not only targeted to the creation of automatic music composers and raters, but also reinforces the research for a deeper understanding of human noesis. The aim of this work is twofold: first, it proposes an Interactive Evolution system that uses Genetic Programming to evolve simple 8–bit melodies. The results obtained by subjective tests indicate that evolution is driven towards more user–preferable sounds. In turn, by monitoring features of the melodies in different evolution stages, indications are provided that some sound features may subsume information about aesthetic criteria. The results are promising and signify that further study of aesthetic preference through Interactive Evolution may accelerate the progress towards defining aesthetic measures for sound and music.
%B Evolutionary and Biologically Inspired Music, Sound, Art and Design
%S Lecture Notes in Computer Science
%I Springer Berlin / Heidelberg
%V 7247
%P 141-152
%@ 978-3-642-29141-8
%G eng
%R 10.1007/978-3-642-29142-5_13
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)
%D 2012
%T Multimodal Optimization Using Niching Differential Evolution with Index-based Neighborhoods
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%X A new family of Differential Evolution mutation strategies (DE/nrand) that are able to handle multimodal functions, have been recently proposed. The DE/nrand family incorporates information regarding the real nearest neighborhood of each potential solution, which aids them to accurately locate and maintain many global optimizers simultaneously, without the need of additional parameters. However, these strategies have increased computational cost. To alleviate this problem, instead of computing the real nearest neighbor, we incorporate an index-based neighborhood into the mutation strategies. The new mutation strategies are evaluated on eight well-known and widely used multimodal problems and their performance is compared against five state-of-the-art algorithms. Simulation results suggest that the proposed strategies are promising and exhibit competitive behavior, since with a substantial lower computational cost they are able to locate and maintain many global optima throughout the evolution process.
%B IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)
%C Brisbane, Australia
%8 June
%G eng
%R 10.1109/CEC.2012.6256480
%0 Book Section
%B Artificial Intelligence: Theories and Applications
%D 2012
%T Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting
%A M. G. Epitropakis
%A D. K. Tasoulis
%A N. G. Pavlidis
%A V. P. Plagianakos
%A M. N. Vrahatis
%E Ilias Maglogiannis
%E Vassilis P. Plagianakos
%E Ioannis Vlahavas
%X Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an “online” algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to reflect changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising.
%B Artificial Intelligence: Theories and Applications
%S Lecture Notes in Computer Science
%I Springer Berlin / Heidelberg
%V 7297
%P 214-222
%@ 978-3-642-30447-7
%G eng
%R 10.1007/978-3-642-30448-4_27
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)
%D 2012
%T Tracking Particle Swarm Optimizers: An adaptive approach through multinomial distribution tracking with exponential forgetting
%A M. G. Epitropakis
%A D. K. Tasoulis
%A N. G. Pavlidis
%A V. P. Plagianakos
%A M. N. Vrahatis
%X An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to capture changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand.
%B IEEE Congress on Evolutionary Computation, 2012. CEC 2012. (IEEE World Congress on Computational Intelligence)
%C Brisbane, Australia
%8 June
%G eng
%R 10.1109/CEC.2012.6256425
%0 Journal Article
%J IEEE Transactions on Evolutionary Computation
%D 2011
%T Enhancing Differential Evolution Utilizing Proximity-based Mutation Operators
%A M. G. Epitropakis
%A D. K. Tasoulis
%A N. G. Pavlidis
%A V. P. Plagianakos
%A M. N. Vrahatis
%X Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.
%B IEEE Transactions on Evolutionary Computation
%V 15
%P 99-119
%G eng
%R 10.1109/TEVC.2010.2083670
%0 Book Section
%B Mathematics and Computation in Music
%D 2011
%T Feature Extraction Using Pitch Class Profile Information Entropy
%A M. A. Kaliakatsos-Papakostas
%A M. G. Epitropakis
%A M. N. Vrahatis
%E Agon, Carlos
%E Andreatta, Moreno
%E Assayag, Gérard
%E Amiot, Emmanuel
%E Bresson, Jean
%E Mandereau, John
%X Computer aided musical analysis has led a research stream to explore the description of an entire musical piece by a single value. Combinations of such values, often called global features, have been used for several identification tasks on pieces with symbolic music representation. In this work we extend some ideas that estimate information entropy of sections of musical pieces, to utilize the Pitch Class Profile information entropy for global feature extraction. Two approaches are proposed and tested, the first approach considers musical sections as overlapping sliding onset windows, while the second one as non-overlapping fixed-length time windows.
%B Mathematics and Computation in Music
%S Lecture Notes in Computer Science
%I Springer Berlin / Heidelberg
%V 6726
%P 354-357
%G eng
%U http://dx.doi.org/10.1007/978-3-642-21590-2_32
%R 10.1007/978-3-642-21590-2_32
%0 Conference Paper
%B IEEE Symposium on Differential Evolution, 2011. SDE 2011. (IEEE Symposium Series on Computational Intelligence)
%D 2011
%T Finding Multiple Global Optima Exploiting Differential Evolution’s Niching Capability
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%X Handling multimodal functions is a very important and challenging task in evolutionary computation community, since most of the real-world applications exhibit highly multi-modal landscapes. Motivated by the dynamics and the proximity characteristics of Differential Evolution's mutation strategies tending to distribute the individuals of the population to the vicinity of the problem's minima, we introduce two new Differential Evolution mutation strategies. The new mutation strategies incorporate spatial information about the neighborhood of each potential solution and exhibit a niching formation, without incorporating any additional parameter. Experimental results on eight well known multimodal functions and comparisons with some state-of-the-art algorithms indicate that the proposed mutation strategies are competitive and very promising, since they are able to reliably locate and maintain many global optima throughout the evolution process.
%B IEEE Symposium on Differential Evolution, 2011. SDE 2011. (IEEE Symposium Series on Computational Intelligence)
%C Paris, France
%8 April
%G eng
%R 10.1109/SDE.2011.5952058
%0 Journal Article
%J International Journal of Bifurcation and Chaos (IJBC)
%D 2011
%T Studying the basin of convergence of methods for computing periodic orbits
%A M. G. Epitropakis
%A M. N. Vrahatis
%X Starting from the well-known Newton's fractal which is formed by the basin of convergence of Newton's method applied to a cubic equation in one variable in the field ℂ, we were able to find methods for which the corresponding basins of convergence do not exhibit a fractal-like structure. Using this approach we are able to distinguish reliable and robust methods for tackling a specific problem. Also, our approach is illustrated here for methods for computing periodic orbits of nonlinear mappings as well as for fixed points of the Poincaré map on a surface of section.
%B International Journal of Bifurcation and Chaos (IJBC)
%V 21
%P 1-28
%G eng
%R 10.1142/S0218127411029653
%0 Conference Paper
%B Applications of Evolutionary Computation
%D 2011
%T Weighted Markov Chain Model for Musical Composer Identification
%A M. A. Kaliakatsos-Papakostas
%A M. G. Epitropakis
%A M. N. Vrahatis
%E Di Chio, Cecilia
%E Brabazon, Anthony
%E Di Caro, Gianni
%E Drechsler, Rolf
%E Farooq, Muddassar
%E Grahl, Jörn
%E Greenfield, Gary
%E Prins, Christian
%E Romero, Juan
%E Squillero, Giovanni
%E Tarantino, Ernesto
%E Tettamanzi, Andrea
%E Urquhart, Neil
%E Uyar, A.
%X Several approaches based on the ‘Markov chain model’ have been proposed to tackle the composer identification task. In the paper at hand, we propose to capture phrasing structural information from inter onset and pitch intervals of pairs of consecutive notes in a musical piece, by incorporating this information into a weighted variation of a first order Markov chain model. Additionally, we propose an evolutionary procedure that automatically tunes the introduced weights and exploits the full potential of the proposed model for tackling the composer identification task between two composers. Initial experimental results on string quartets of Haydn, Mozart and Beethoven suggest that the proposed model performs well and can provide insights on the inter onset and pitch intervals on the considered musical collection.
%B Applications of Evolutionary Computation
%I Springer Berlin / Heidelberg
%G eng
%R 10.1007/978-3-642-20520-0_34
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2010. CEC 2010. (IEEE World Congress on Computational Intelligence)
%D 2010
%T Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%K cognitive experience
%K convergence
%K differential evolution
%K evolutionary computation
%K particle swarm optimisation
%K particle swarm optimization
%K social experience
%X In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. Motivated by the behavior and the proximity characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid approach that combines the Particle Swarm Optimization and the Differential Evolution algorithm. Particle Swarm Optimization has the tendency to distribute the best personal positions of the swarm near to the vicinity of problem’s optima. In an attempt to efficiently guide the evolution and enhance the convergence, we evolve the personal experience of the swarm with the Differential Evolution algorithm. Extensive experimental results on twelve high dimensional multimodal benchmark functions indicate that the hybrid variants are very promising and improve the original algorithm.
%B IEEE Congress on Evolutionary Computation, 2010. CEC 2010. (IEEE World Congress on Computational Intelligence)
%C Barcelona, Spain
%8 July
%G eng
%R 10.1109/CEC.2010.5585967
%0 Journal Article
%J Applied Soft Computing
%D 2010
%T Hardware-Friendly Higher-Order Neural Network Training Using Distributed Evolutionary Algorithms
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%K Higher-Order Neural Networks
%X In this paper, we study the class of Higher-Order Neural Networks and especially the Pi-Sigma Networks. The performance of Pi-Sigma Networks is evaluated through several well known Neural Network Training benchmarks. In the experiments reported here, Distributed Evolutionary Algorithms are implemented for Pi-Sigma neural networks training. More specifically the distributed versions of the Differential Evolution and the Particle Swarm Optimization algorithms have been employed. To this end, each processor is assigned a subpopulation of potential solutions. The subpopulations are independently evolved in parallel and occasional migration is employed to allow cooperation between them. The proposed approach is applied to train Pi-Sigma Networks using threshold activation functions. Moreover, the weights and biases were confined to a narrow band of integers, constrained in the range [-32,32]. Thus, the trained Pi-Sigma neural networks can be represented by using 6 bits. Such networks are better suited than the real weight ones for hardware implementation and to some extend are immune to low amplitude noise that possibly contaminates the training data. Experimental results suggest that the proposed training process is fast, stable and reliable and the distributed trained Pi-Sigma Networks exhibited good generalization capabilities.
%B Applied Soft Computing
%V 10
%P 398-408
%G eng
%R 10.1016/j.asoc.2009.08.010
%0 Conference Paper
%B Applications of Evolutionary Computation
%D 2010
%T Musical Composer Identification through Probabilistic and Feedforward Neural Networks
%A M. A. Kaliakatsos-Papakostas
%A M. G. Epitropakis
%A M. N. Vrahatis
%E Di Chio, Cecilia
%E Brabazon, Anthony
%E Di Caro, Gianni
%E Ebner, Marc
%E Farooq, Muddassar
%E Fink, Andreas
%E Grahl, Jörn
%E Greenfield, Gary
%E Machado, Penousal
%E O’Neill, Michael
%E Tarantino, Ernesto
%E Urquhart, Neil
%X During the last decade many efforts for music information retrieval have been made utilizing Computational Intelligence methods. Here, we examine the information capacity of the Dodecaphonic Trace Vector for composer classification and identification. To this end, we utilize Probabilistic Neural Networks for the construction of a similarity matrix of different composers and analyze the Dodecaphonic Trace Vector’s ability to identify a composer through trained Feedforward Neural Networks. The training procedure is based on classical gradient-based methods as well as on the Differential Evolution algorithm. An experimental analysis on the pieces of seven classical composers is presented to gain insight about the most important strengths and weaknesses of the aforementioned approach.
%B Applications of Evolutionary Computation
%I Springer Berlin / Heidelberg
%G eng
%R 10.1007/978-3-642-12242-2\_42
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2009. CEC 2009
%D 2009
%T Evolutionary Adaptation of the Differential Evolution Control Parameters
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%K adaptive control
%K differential evolution control parameter
%K evolutionary adaptation
%K evolutionary computation
%K optimisation
%K optimization
%K self-adaptive differential evolution algorithm
%K self-adjusting systems
%K user-defined parameter tuning
%X This paper proposes a novel self-adaptive scheme for the evolution of crucial control parameters in evolutionary algorithms. More specifically, we suggest to utilize the differential evolution algorithm to endemically evolve its own control parameters. To achieve this, two simultaneous instances of Differential Evolution are used, one of which is responsible for the evolution of the crucial user-defined mutation and recombination constants. This self-adaptive differential evolution algorithm alleviates the need of tuning these user-defined parameters while maintains the convergence properties of the original algorithm. The evolutionary self-adaptive scheme is evaluated through several well-known optimization benchmark functions and the experimental results indicate that the proposed approach is promising.
%B IEEE Congress on Evolutionary Computation, 2009. CEC 2009
%C Trondheim, Norway
%8 May
%G eng
%R 10.1109/CEC.2009.4983102
%0 Book Section
%B Artificial Higher Order Neural Networks for Computer Science and Engineering: Tends for Emerging Applications
%D 2009
%T Evolutionary Algorithm Training of Higher-Order Neural Networks
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%E Ming Zhang
%B Artificial Higher Order Neural Networks for Computer Science and Engineering: Tends for Emerging Applications
%I IGI Global
%G eng
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)
%D 2008
%T Balancing the exploration and exploitation capabilities of the Differential Evolution Algorithm
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%K differential evolution algorithm
%K evolutionary computation
%K optimization
%K search problems
%K self-balancing hybrid mutation operator
%X The hybridization and composition of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper we propose a hybrid approach that combines differential evolution mutation operators in an attempt to balance their exploration and exploitation capabilities. Additionally, a self-balancing hybrid mutation operator is presented, which favors the exploration of the search space during the first phase of the optimization, while later opts for the exploitation to aid convergence to the optimum. Extensive experimental results indicate that the proposed approaches effectively enhance DEpsilas ability to accurately locate solutions in the search space.
%B IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)
%C Hong Kong
%8 June
%G eng
%R 10.1109/CEC.2008.4631159
%0 Conference Paper
%B Proceedings of the 10th annual inproceedings on Genetic evolutionary computation, GECCO 2008
%D 2008
%T Non-Monotone Differential Evolution
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%X The Differential Evolution algorithm uses an elitist selection, constantly pushing the population in a strict downhill search, in an attempt to guarantee the conservation of the best individuals. However, when this operator is combined with an exploitive mutation operator can lead to premature convergence to an undesired region of attraction. To alleviate this problem, we propose the Non-Monotone Differential Evolution algorithm. To this end, we allow the best individual to perform some uphill movements, greatly enhancing the exploration of the search space. This approach further aids algorithm’s ability to escape undesired regions of the search space and improves its performance. The proposed approach utilizes already computed pieces of information and does not require extra function evaluations. Experimental results indicate that the proposed approach provides stable and reliable convergence." keywords = "differential evolution, evolutionary algorithms, global optimization, non-monotone differential evolution
%B Proceedings of the 10th annual inproceedings on Genetic evolutionary computation, GECCO 2008
%I ACM
%C New York, NY, USA
%@ 978-1-60558-130-9
%G eng
%R http://doi.acm.org/10.1145/1389095.1389195
%0 Conference Paper
%B IEEE Congress on Evolutionary Computation, 2007. CEC 2007
%D 2007
%T Computational Intelligence Algorithms For Risk-Adjusted Trading Strategies
%A N. G. Pavlidis
%A E. G. Pavlidis
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%K computational intelligence algorithm
%K differential evolution
%K financial market
%K foreign exchange market
%K foreign exchange trading
%K generalized moving average rule
%K genetic algorithms
%K genetic programming
%K optimization
%K pattern detection
%K risk analysis
%K risk-adjusted trading strategy
%K statistical testing
%X This paper investigates the performance of trading strategies identified through computational intelligence techniques. We focus on trading rules derived by genetic programming, as well as, generalized moving average rules optimized through differential evolution. The performance of these rules is investigated using recently proposed risk-adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but genetic programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.
%B IEEE Congress on Evolutionary Computation, 2007. CEC 2007
%C Singapore
%8 September
%G eng
%R 10.1109/CEC.2007.4424517
%0 Conference Paper
%B International Conference of Numerical Analysis and Applied Mathematics
%D 2006
%T Higher-Order Neural Networks Training Using Differential Evolution
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%B International Conference of Numerical Analysis and Applied Mathematics
%I Wiley-VCH
%C Hersonissos, Crete, Greece
%G eng
%0 Conference Paper
%B International Conference of Computational Methods in Sciences and Engineering
%D 2006
%T Integer Weight Higher-Order Neural Network Training Using Distributed Differential Evolution
%A M. G. Epitropakis
%A V. P. Plagianakos
%A M. N. Vrahatis
%B International Conference of Computational Methods in Sciences and Engineering
%I LSCCS
%C Crete, Greece
%G eng
%0 Journal Article
%J ACM SIGSAM Bulleting
%D 2005
%T Root finding and approximation approaches through neural networks
%A M. G. Epitropakis
%A M. N. Vrahatis
%X In this paper, we propose two approaches to approximate high order multivariate polynomials and to estimate the number of roots of high order univariate polynomials. We employ high order neural networks such as Ridge Polynomial Networks and Pi -- Sigma Networks, respectively. To train the networks efficiently and effectively, we recommend the application of stochastic global optimization techniques. Finally, we propose a two step neural network based technique, to estimate the number of roots of a high order univariate polynomial.
%B ACM SIGSAM Bulleting
%V 39
%P 118-121
%G eng
%R http://doi.acm.org/10.1145/1140378.1140382