Genetic algorithms: a bibliography.
Goldberg, D.E., Zakrzewski, K., Sutton, B., Gadient, R., Chang, C., Gallego, P., Miller, B., Cantú-Paz, E. (1997)TR No.: 97011 | Download PDF | Download PS
Abstract:
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Posted: February 24th, 1997 under Genetic algorithms. Comments: none
Compressed introns in a linkage learning genetic algorithm.
Lobo, F. G., Deb, K., Goldberg, D.E., Harik, G., and Wang, L. (1997)TR No.: 97010 | Download PDF | Download PS
Abstract:
By: Fernando G. Lobo, Kalyanmoy Deb, David E. Goldberg, Georges R. Harik, and Liwei Wang
Abstract:
Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This paper revisits and extends the latest of such efforts—the linkage learning genetic algorithm. Specifically, it introduces an efficient mechanism for representing the non-coding material. […]
Posted: February 20th, 1997 under Genetic algorithms. Comments: none
Genetic algorithms at the University of Illinois.
Goldberg, D.E. (ed.) (1997)TR No.: 97009 | Download PDF | Download PS
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Posted: February 16th, 1997 under Genetic algorithms. Comments: none
The nature of niching: genetic algorithms and he evolution of optimal, cooperative populations.
Horn, J. (1997)TR No.: 97008 | Download PDF | Download PS
Abstract:
Genetic algorithms (GAs) with fitness sharing have been analyzed and successfully applied to problems in search and optimization, while GAs using various types of resource sharing have been incorporated into classifiers, immune system models, artificial ecologies, artificial economies, etc. Both types of sharing are based on the same observation of nature: dividing a finite resource […]
Posted: February 12th, 1997 under Genetic algorithms. Comments: none
Adaptive niching via coevolutionary sharing. 17pp
Goldberg, D.E. and Wang, L. (1997)TR No.: 97007 | Download PDF | Download PS
Abstract:
Related PostsThe nature of niching: genetic algorithms and he evolution of optimal, cooperative populations.An Adaptive Sampling Scheme for Genetic Algorithms on the Sampled OneMax ProblemSelf-Adaptive Mutation in XCSF
Posted: February 8th, 1997 under Genetic algorithms. Comments: none
The compact genetic algorithm. 21pp.
Harik, G., Lobo, F., and Goldberg, D.E. (1997)TR No.: 97006 | Download PDF | Download PS
Abstract:
This paper introduces the compact genetic algorithm (cGA). The cGA represents the population as a probability distribution over the set of solutions, and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. Therefore, it can be used […]
Posted: February 4th, 1997 under Genetic algorithms. Comments: none
Learning linkage to efficiently solve problems of bounded difficulty using genetic algorithms. 135pp
Harik, G. (1997)TR No.: 97005 | Download PDF | Download PS
Abstract:
The complicated nature of modern scientific endeavors often times requires the employment of black-box optimization. For the past twenty years, the simple genetic algorithm (sGA) has proven to be a fertile inspiration for such techniques. Yet, many attempts to improve or adapt the sGA remain disconnected with its prevailing theory. This theory suggests that the […]
Posted: January 24th, 1997 under Genetic algorithms. Comments: none
Designing efficient master-slave parallel genetic algorithms. 12pp.
Cantú-Paz, E. (1997)TR No.: 97004 | Download PDF | Download PS
Abstract:
A simple technique to reduce the execution time of genetic algorithms (GAs) is to divide the task of evaluating the population among several processors. This class of algorithms is called “global” parallel GAs because selection and mating consider the entire population. Global parallel GAs are usually implemented as master-slave programs and require constant interprocessor communication. […]
Posted: January 20th, 1997 under Genetic algorithms. Comments: none
A Survey of Parallel Genetic Algorithms. 28pp.
Cantú-Paz, E. (1997)TR No.: 97003 | Download PDF | Download PS
Abstract:
Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance and as such there has been extensive research in this field. This paper attempts to collect, organize, and present in a unified way some […]
Posted: January 16th, 1997 under Genetic algorithms. Comments: none
Genetic algorithms: a bibliography. 233pp.
Goldberg, D. E., Zakrzewski, K., Chang, C., Gallego, P., Sutton, B., Miller, B.L., and Cantú-Paz, E. (1997)TR No.: 97002 | Download PDF | Download PS
Abstract:
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Posted: January 12th, 1997 under Genetic algorithms. Comments: none