Multi-Population Genetic Algorithm for Locating Multi-Optima in Noisy Complex Landscape

• Published in: Communications in statistics. Theory and methods Vol. 40; no. 16; pp. 3029 - 3048
• Main Authors: Szeto, K. Y., Guo, Yunbo
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Philadelphia, PA Taylor & Francis Group 15.08.2011; Taylor & Francis
• Subjects: 68Wxx; Applications; Biology, psychology, social sciences; Exact sciences and technology; General topics; Insurance, economics, finance; Landscape mapping; Markov processes; Mathematics; Multi-agent system; Parallel genetic algorithm; Population dynamics; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Statistics; Complex function; Polynomial; Parallel algorithm; Multi-agent system; Fitting; Covariance analysis; 68Wxx; Resource allocation; Average; Economic sciences; Variance analysis; Mean estimation; Population genetics; 65C50; Variance; Landscape mapping; Statistical method; Statistical regression; Algorithm performance; Genetic algorithm; Environment; Parallel genetic algorithm; Population dynamics; Econometrics

A multi-population genetic algorithm (MPGA) is introduced to search for as many as possible of the local optima of a complex function in a noisy environment. By considering a multi-agent system consisting of sub-populations of agents that evolve simultaneously as a group of chromosomes in a genetic...