NDLI: A novel genetic variable representation for dynamic optimization problems in evolutionary computation

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A novel genetic variable representation for dynamic optimization problems in evolutionary computation

Content Provider	ACM Digital Library
Author	Liang, Yong
Abstract	This paper introduces a novel genetic variable representation for dynamic optimization problems in evolutionary computation. This variable representation allows static evolutionary optimization approaches to be extended to efficiently explore global and better local optimal areas in dynamic fitness landscapes. It represents a single individual as a pair of real-valued vector (x, r) ∈ $R^{n}$ x $R^{2}$ in the evolutionary search population. The first vector x corresponds to a point in the n-dimensional search space (an object variable vector), while the second vector r represents the dynamic fitness value and the dynamic tendency of the individual x in the dynamic environment. r is the control variable (also called strategy variable), which allow self-adaptation. The object variable vector x is operated by different genetic strategies according to its corresponding r. As a case study, we have integrated the new variable representation into Genetic Algorithms (GAs), yielding an Dynamic Optimization Genetic Algorithm (DOGA). DOGA is experimentally tested with 5 benchmark dynamic problems. The results all demonstrate that DOGA consistently outperforms other GAs on dynamic optimization problems.
Starting Page	45
Ending Page	52
Page Count	8
File Format	PDF
ISBN	9781605586625
DOI	10.1145/1644993.1645002
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-08-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dynamic optimization Evolutionary computation Genetic algorithms
Content Type	Text
Resource Type	Article

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