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A Comparative Analysis of Evolutionary Algorithms for Function Optimisation (1996)
| Venue: | IN PROCS. OF THE SECOND WORKSHOP ON EVOLUTIONARY COMPUTING (WEC2 |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Falco96acomparative,
author = {De Falco and R. Del Balio and A. Della Cioppa and E. Tarantino},
title = {A Comparative Analysis of Evolutionary Algorithms for Function Optimisation},
booktitle = {IN PROCS. OF THE SECOND WORKSHOP ON EVOLUTIONARY COMPUTING (WEC2},
year = {1996},
pages = {29--32},
publisher = {}
}
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Abstract
In this paper the Breeder Genetic Algorithms are compared against both serial and parallel Genetic Algorithms by using a wide range of optimisation functions taken from the literature. The aim is to investigate how the change of the fitness function influences their problem-- solving capabilities. The experimental findings show that the Breeder Genetic Algorithms outperform the Genetic Algorithms considered in terms of number of evaluations and robustness. I. Introduction A wide variety of heuristic techniques has been proposed to solve different kinds of optimisation problems. Among these, the Genetic Algorithms (GAs) [1; 2] and their parallel versions [3] should be mentioned. They have proved to be able to efficiently solve difficult optimisation problems. Recently a novel technique, the Breeder Genetic Algorithms (BGAs) [4; 5], particularly suitable to deal with continuous optimisation parameters, has been introduced. BGAs are based on the evolution model typical of GAs, however th...
Keyphrases
evolutionary algorithm comparative analysis function optimisation breeder genetic algorithm genetic algorithm optimisation problem different kind evolution model experimental finding parallel version fitness function heuristic technique continuous optimisation parameter optimisation function wide variety novel technique parallel genetic algorithm wide range difficult optimisation problem
