NDLI: Landscape ruggedness in evolutionary algorithms

Content Provider	IEEE Xplore Digital Library
Author	Kolarov, K.
Copyright Year	1997
Description	Author affiliation: Interval Res. Corp., Palo Alto, CA, USA (Kolarov, K.)
Abstract	The paper describes a model for the exploration of the dynamics of interaction in a population of individuals using an evolutionary approach. In particular we analyze the effect of the complexity of the fitness landscape and the population size on the performance of an evolutionary algorithm in terms of speed of fixation and fixation to sub optimal individuals. Our evolutionary model resembles those in the field of population genetics and approaches evolution as a process of adaptation rather than an optimization, The simulation results from our experiments are justified with a theoretical probabilistic analysis of the dynamics of the population with and without recombination. The paper introduces several new insights into the interaction and roles of the different parameters of an evolutionary system. There have been very few attempts to theoretically analyze the dynamics of interaction between the different operators and the performance of GA. We give a detailed description of our model. The experiment for which we vary the ruggedness of the landscape is described. To better understand the results presented, we perform a theoretical analysis of the model and derive recursive formulas describing the dynamics of interaction in the model. These results are explained and some conclusions are drawn.
Starting Page	19
Ending Page	24
File Size	639256
Page Count	6
File Format	PDF
ISBN	0780339495
DOI	10.1109/ICEC.1997.592261
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-04-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Evolutionary computation Performance analysis Genetic mutations Biological cells Character generation Milling machines Algorithm design and analysis Analytical models Organisms Testing
Content Type	Text
Resource Type	Article

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