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Author Kirley, Michael ♦ Green, David G.
Source CiteSeerX
Content type Text
Publisher Morgan Kaufmann
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Pseudo Landscape ♦ Novel Approach ♦ Intermediate Disturbance ♦ Robust Solution ♦ Dynamic Spatial Structure ♦ Optimal Solution ♦ Population Diversity ♦ Non-stationary Problem ♦ Good Solution ♦ Fluctuating Fitness Landscape ♦ Empirical Investigation ♦ Cellular Genetic Algorithm ♦ Dynamic Environment ♦ Many Real-world Optimisation Problem ♦ Simulation Result ♦ Static Instance ♦ Isolated Subpopulation ♦ Benchmark Problem
Description Many real-world optimisation problems are dynamic. For such problems the goal is to track the progression of optimal solutions across the fluctuating fitness landscape rather than to find an exceptionally good solution for a static instance of the problem. Here we present a novel approach for creating robust solutions for non-stationary problems using the Cellular Genetic Algorithm (CGA). The CGA maps the evolving population of solutions onto a pseudo landscape. Intermediate disturbances (disasters) are introduced that break down the connectivity in the pseudo landscape, leading to isolated subpopulations. The dynamic spatial structure of the CGA helps to maintain population diversity. We investigate the performance of the algorithm using a proposed benchmark problem. Simulation results indicate that the CGA is able to respond and adapt effectively to the dynamic environment. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2000-01-01
Publisher Institution In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 200) (2000