@article {3,
	title = {Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach},
	journal = {Information Sciences},
	volume = {216},
	year = {2012},
	pages = {50-92},
	abstract = {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{\textquoteright}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.},
	issn = {0020-0255},
	doi = {10.1016/j.ins.2012.05.017},
	author = {M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis}
}
@article {5,
	title = {Enhancing Differential Evolution Utilizing Proximity-based Mutation Operators},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {15},
	year = {2011},
	pages = {99-119},
	abstract = {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.},
	issn = {1089-778X},
	doi = {10.1109/TEVC.2010.2083670},
	author = {M. G. Epitropakis and D. K. Tasoulis and N. G. Pavlidis and V. P. Plagianakos and M. N. Vrahatis}
}
@conference {16,
	title = {Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution},
	booktitle = {IEEE Congress on Evolutionary Computation, 2010. CEC 2010. (IEEE World Congress on Computational Intelligence)},
	year = {2010},
	month = {July},
	address = {Barcelona, Spain},
	abstract = {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{\textquoteright}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.},
	keywords = {cognitive experience, convergence, differential evolution, evolutionary computation, particle swarm optimisation, particle swarm optimization, social experience},
	doi = {10.1109/CEC.2010.5585967},
	author = {M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis}
}
@conference {18,
	title = {Evolutionary Adaptation of the Differential Evolution Control Parameters},
	booktitle = {IEEE Congress on Evolutionary Computation, 2009. CEC 2009},
	year = {2009},
	month = {May},
	address = {Trondheim, Norway},
	abstract = {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.},
	keywords = {adaptive control, differential evolution control parameter, evolutionary adaptation, evolutionary computation, optimisation, optimization, self-adaptive differential evolution algorithm, self-adjusting systems, user-defined parameter tuning},
	doi = {10.1109/CEC.2009.4983102},
	author = {M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis}
}
@inbook {24,
	title = {Evolutionary Algorithm Training of Higher-Order Neural Networks},
	booktitle = {Artificial Higher Order Neural Networks for Computer Science and Engineering: Tends for Emerging Applications},
	year = {2009},
	publisher = {IGI Global},
	organization = {IGI Global},
	author = {M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis},
	editor = {Ming Zhang}
}
