%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 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 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 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 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 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