# Where Genetic Algorithms Excel

@article{Baum2001WhereGA, title={Where Genetic Algorithms Excel}, author={Eric B. Baum and Dan Boneh and Charles Garrett}, journal={Evolutionary Computation}, year={2001}, volume={9}, pages={93-124} }

We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP. [... ] Key Method GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic… Expand

## 30 Citations

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The analysis of additively-separable functions suggests that, in most cases under the constant cost constraint, a single run with the largest population possible reaches a better solution than multiple independent runs.

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## References

SHOWING 1-10 OF 51 REFERENCES

On genetic algorithms

- Computer ScienceCOLT '95
- 1995

C Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach in some regimes, and some new large deviation bounds on this submartingale enable us to determine the running time of the algorithm.

Genetic Algorithms Foundations of Genetic Algorithm

- Computer Science

GA has been theoretically and empirically proved to provide a robust search in complex search spaces and to keep a balance between exploitation and exploration in its search to the optimal solution for survival in many different environments.

An introduction to genetic algorithms

- Computer Science
- 1996

An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.

An Overview of Evolutionary Algorithms for Parameter Optimization

- Computer ScienceEvolutionary Computation
- 1993

Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs),…

When will a Genetic Algorithm Outperform Hill Climbing

- Computer ScienceNIPS
- 1993

An "idealized" genetic algorithm (IGA) is analyzed that is significantly faster than RMHC and that gives a lower bound for GA speed.

Genetic Algorithms in Search Optimization and Machine Learning

- Computer Science
- 1988

This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.

Efficiency of truncation selection.

- BiologyProceedings of the National Academy of Sciences of the United States of America
- 1979

It is shown, for mutations affecting viability in Drosophila, that truncation selection or reasonable departures therefrom can reduce the mutation load greatly, and this may be one way to reconcile the very high mutation rate of such genes with a small mutation load.

ASYMPTOTIC CONVERGENCE PROPERTIES OF GENETIC ALGORITHMS AND EVOLUTIONARY PROGRAMMING: ANALYSIS AND EXPERIMENTS

- Computer Science
- 1994

The basic convergence properties of evolutionary optimization algorithms are investigated and it is indicated that the methods studied will asymptotically converge to global optima and genetic algorithms may prematurely stagnate at solutions that may not even be locally optimal.

Toward a Theory of Evolution Strategies: On the Benefits of Sex the (/, ) Theory

- BiologyEvolutionary Computation
- 1995

The power of sexuality is discussed and it is shown that this power does not stem mainly from the combination of good properties of the mates but rather from genetic repair diminishing the influence of harmful mutations.