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GECCO
2006
Springer
205views Optimization» more  GECCO 2006»
13 years 8 months ago
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP
In contrast with the diverse array of genetic algorithms, the Genetic Programming (GP) paradigm is usually applied in a relatively uniform manner. Heuristics have developed over t...
L. Darrell Whitley, Marc D. Richards, J. Ross Beve...
EC
2002
228views ECommerce» more  EC 2002»
13 years 4 months ago
Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Gen
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we pre...
Rajeev Kumar, Peter Rockett
ISCI
2008
58views more  ISCI 2008»
13 years 4 months ago
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Manuel Lozano, Francisco Herrera, José Ram&...
GECCO
2007
Springer
183views Optimization» more  GECCO 2007»
13 years 10 months ago
Distribution replacement: how survival of the worst can out perform survival of the fittest
A new family of "Distribution Replacement” operators for use in steady state genetic algorithms is presented. Distribution replacement enforces the members of the populatio...
Howard Tripp, Phil Palmer
GECCO
2006
Springer
130views Optimization» more  GECCO 2006»
13 years 8 months ago
An efficient multi-objective evolutionary algorithm with steady-state replacement model
The generic Multi-objective Evolutionary Algorithm (MOEA) aims to produce Pareto-front approximations with good convergence and diversity property. To achieve convergence, most mu...
Dipti Srinivasan, Lily Rachmawati