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ENGL
2007
121views more  ENGL 2007»
15 years 3 months ago
A Comparison between Genetic Algorithms and Evolutionary Programming based on Cutting Stock Problem
—Genetic Algorithms (GA) and Evolutionary Programming (EP) are two well-known optimization methods that belong to the class of Evolutionary Algorithms (EA). Both methods have gen...
Raymond Chiong, Ooi Koon Beng
TCS
2008
15 years 3 months ago
The cost of offline binary search tree algorithms and the complexity of the request sequence
In evaluating the performance of online algorithms for search trees, one wants to compare them to the best offline algorithm available. In this paper we lower bound the cost of an...
Jussi Kujala, Tapio Elomaa
PPSN
2010
Springer
15 years 1 months ago
Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis
We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the `best' one? and the second one:...
Olaf Mersmann, Mike Preuss, Heike Trautmann
GECCO
1999
Springer
15 years 7 months ago
A parameter-less genetic algorithm
From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes,...
Georges R. Harik, Fernando G. Lobo
MICRO
1990
IEEE
147views Hardware» more  MICRO 1990»
15 years 7 months ago
Motivation and framework for using genetic algorithms for microcode compaction
Genetic algorithms are a robust adaptive optimization technique based on a biological paradigm. They perform efficient search on poorly-defined spaces by maintaining an ordered po...
Steven J. Beaty, Darrell Whitley, Gearold Johnson