Evaluating Evolutionary Algorithms

Evaluating Evolutionary Algorithms (1996)

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by D. Whitley , K. Mathias , S. Rana , J. Dzubera

Venue:	Artificial Intelligence
Citations:	75 - 14 self

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Datum	Value	Source
TITLE	Evaluating Evolutionary Algorithms	user correction - Legacy Corrections
AUTHOR NAME	D. Whitley	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Colorado State University	user correction - Legacy Corrections
AUTHOR ADDR	Fort Collins, Colorado 80523 USA	user correction - Legacy Corrections
AUTHOR NAME	K. Mathias	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Colorado State University	user correction - Legacy Corrections
AUTHOR ADDR	Fort Collins, Colorado 80523 USA	user correction - Legacy Corrections
AUTHOR NAME	S. Rana	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Colorado State University	user correction - Legacy Corrections
AUTHOR ADDR	Fort Collins, Colorado 80523 USA	user correction - Legacy Corrections
AUTHOR NAME	J. Dzubera	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science; Colorado State University	user correction - Legacy Corrections
AUTHOR ADDR	Fort Collins, Colorado 80523 USA	user correction - Legacy Corrections
ABSTRACT	Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hill-climbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of...	user correction - Legacy Corrections
YEAR	1996	INFERENCE
VENUE	Artificial Intelligence	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	245--276	INFERENCE
VOLUME	85	INFERENCE
CITATIONS	43 found	ParsCit 1.0