Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms

Authors:
Alexandru Agapie
Affiliations:
Laboratory of Computational Intelligence, Institute for Microtechnologies, Bucharest, P.O. Box 38-160, 72225, Romania
Venue:
Evolutionary Computation
Year:
2001

Citing 8
Cited 5

An overview of parameter control methods by self-adaption in evolutionary algorithms

Fundamenta Informaticae
Finite Markov chain results in evolutionary computation: a tour d'horizon

Fundamenta Informaticae
The Simple Genetic Algorithm: Foundations and Theory

The Simple Genetic Algorithm: Foundations and Theory
Modelling Genetic Algorithms: From Markov Chains to Dependence with Complete Connections

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms: Minimal Conditions for Convergence

AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Toward a theory of evolution strategies: Self-adaptation

Evolutionary Computation
Rigorous hitting times for binary mutations

Evolutionary Computation
Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis

IEEE Transactions on Neural Networks

Locally-adaptive and memetic evolutionary pattern search algorithms

Evolutionary Computation
Evolutionary algorithms with deterministic mutation operators used for the optimization of the trajectory of a four-bar mechanism

Mathematics and Computers in Simulation
Theory of genetic algorithms II: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling

Theoretical Computer Science
An Analysis About the Asymptotic Convergence of Evolutionary Algorithms

Computational Intelligence and Security
About the limit behaviors of the transition operators associated with EAs

ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections – accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.