@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}
}
@conference {19,
	title = {Balancing the exploration and exploitation capabilities of the Differential Evolution Algorithm},
	booktitle = {IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)},
	year = {2008},
	month = {June},
	address = {Hong Kong},
	abstract = {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.},
	keywords = {differential evolution algorithm, evolutionary computation, optimization, search problems, self-balancing hybrid mutation operator},
	doi = {10.1109/CEC.2008.4631159},
	author = {M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis}
}
