Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Journal of Global Optimization
Breeding swarms: a GA/PSO hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Dejong Function Optimization by Means of a Parallel Approach to Fuzzified Genetic Algorithm
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Super-fit control adaptation in memetic differential evolution frameworks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Parallel hybrid PSO-GA algorithm and its application to layout design
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fast Adaptive Memetic Algorithm for Online and Offline Control Design of PMSM Drives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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There are various kinds of evolutionary computations (ECs) and they have their own merits and demerits. For example, PSO (Particle Swarm Optimization) shows high ability during initial period in general, whereas DE (Differential Evolution) shows high ability especially in the latter period in search to find more accurate solutions. This paper proposes a novel and integrated framework to effectively combine the merits of several evolutionary computations. There are five distinctive features in the proposed framework. 1) There are several individual pools, and each pool corresponds to one EC. 2) Parents do not necessarily belong to the same EC: for example, a GA type individual can be a spouse of a PSO type individual. 3) Each incorporated EC has its own evaluated value (EV), and it changes according to the best fitness value at each generation. 4) The number of individuals in each EC changes according to the EV. 5) All of the individuals have their own lifetime to avoid premature convergence; when an individual meets lifetime, the individual reselect EC, and the probability of each EC to be selected depends on the EV. In the proposed framework, therefore, more individuals are allotted to the ECs which show higher performance than the other at each generation: effective usage of individuals is enabled. In this way, this framework can make use of merits of incorporated ECs. Original GA, original PSO and original DE are used to construct a simple proposed framework-based system. We carried out experiments using well-known benchmark functions. The results show that the new system outperformed there incorporated ECs in 9 functions out of 13 functions.