@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 {21,
	title = {Computational Intelligence Algorithms For Risk-Adjusted Trading Strategies},
	booktitle = {IEEE Congress on Evolutionary Computation, 2007. CEC 2007},
	year = {2007},
	month = {September},
	address = {Singapore},
	abstract = {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.},
	keywords = {computational intelligence algorithm, differential evolution, financial market, foreign exchange market, foreign exchange trading, generalized moving average rule, genetic algorithms, genetic programming, optimization, pattern detection, risk analysis, risk-adjusted trading strategy, statistical testing},
	doi = {10.1109/CEC.2007.4424517},
	author = {N. G. Pavlidis and E. G. Pavlidis and M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis}
}
