Efficiency speed-up strategies for evolutionary computation: fundamentals and fast-GAs
Applied Mathematics and Computation
Theoretical analysis of genetic algorithms in noisy environments based on a Markov Model
Proceedings of the 10th annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Hybrid solution algorithms for task scheduling problem with moving executors
Engineering Applications of Artificial Intelligence
A hybrid immune algorithm with information gain for the graph coloring problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A theoretical model and convergence analysis of memetic evolutionary algorithms
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A clonal selection algorithm for coloring, hitting set and satisfiability problems
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Convergence analysis and improvements of quantum-behaved particle swarm optimization
Information Sciences: an International Journal
Preserving variability in sexual multi-agent systems with diploidy and dominance
ESAW'05 Proceedings of the 6th international conference on Engineering Societies in the Agents World
Computational Optimization and Applications
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There have been various algorithms designed for simulating natural evolution. This paper proposes a new simulated evolutionary computation model called the abstract evolutionary algorithm (AEA), which unifies most of the currently known evolutionary algorithms and describes the evolution as an abstract stochastic process composed of two fundamental operators: selection and evolution operators. By axiomatically characterizing the properties of the fundamental selection and evolution operators, several general convergence theorems and convergence rate estimations for the AEA are established. The established theorems are applied to a series of known evolutionary algorithms, directly fielding new convergence conditions and convergence rate estimations of various specific genetic algorithms and evolutionary strategies. The present work provides a significant step toward the establishment of a unified theory of simulated evolutionary computation