The initial population of an evolutionary algorithm is an important factor which affects the
convergence rate and ultimately its ability to find high quality solutions or satisfactory …

This article proposes a procedure to perform an intelligent initialization for population-based
algorithms. The proposed pre-processing procedure, namely Cluster-Based Population …

The analysis of the performance of different approaches is a staple concern in the design of
Computational Intelligence experiments. Any proper analysis of evolutionary optimization …

Premature convergence is one of the best-known drawbacks that affects the performance of
evolutionary algorithms. An alternative for dealing with this problem is to explicitly try to …

It is well known that the performances of evolutionary algorithms are influenced by the
quality of their initial populations. Over the years, many different techniques for generating …

While a lot of attention is usually devoted to the study of different components of evolutionary
algorithms or the creation of heuristic operators, little effort is being directed at how these …

In this paper, we propose a replacement strategy for steady-state genetic algorithms that
takes into account two features of the element to be included into the population: a measure …

A retrospective view and outlook on evolutionary algorithms Page 1 A Retrospective View and
Outlook on Evolutionary Algorithms Lawrence J. Fogel Natural Selection, Inc. 3333 North Torrey …

Evolution strategies are a class of general optimisation algorithms which are applicable to
functions that are multimodal, non-differentiable, or even discontinuous. Although …

Evolution strategies are general, nature-inspired heuristics for search and optimization. Due
to their use of populations of candidate solutions and their advanced adaptation schemes …