Genetic algorithms: a survey
- 1 June 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Computer
- Vol. 27 (6) , 17-26
- https://doi.org/10.1109/2.294849
Abstract
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception.<>Keywords
This publication has 6 references indexed in Scilit:
- Adaptive probabilities of crossover and mutation in genetic algorithmsIEEE Transactions on Systems, Man, and Cybernetics, 1994
- What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their ExplanationMachine Learning, 1993
- Genetic Algorithms + Data Structures = Evolution ProgramsPublished by Springer Nature ,1992
- GENITOR II: a distributed genetic algorithmJournal of Experimental & Theoretical Artificial Intelligence, 1990
- Optimization of Control Parameters for Genetic AlgorithmsIEEE Transactions on Systems, Man, and Cybernetics, 1986
- Optimization by Simulated AnnealingScience, 1983