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Searching under multievolutionary pressures
Searching Under Multi-Evolutionary Pressures
Hussein A. Abbass and Kalyanmoy Deb
Artificial Life and Adaptive Robotics (A.L.A.R.) Lab, School of Computer Science,
University of New South Wales, Australian Defence Force Academy Campus,
Canberra, Australia. h.abbass@adfa.edu.au
Mechanical Engineering Department, Indian Institute of Technology, Kanpur,
Kanpur, PIN 208 016, India. deb@iitk.ac.in
Abstract. A number of authors made the claim that a multiobjective
approach preserves genetic diversity better than a single objective ap-
proach. Sofar, none of these claims presented a thorough analysis to the
effect of multiobjective approaches. In this paper, we provide such anal-
ysis and show that a multiobjective approach does preserve reproductive
diversity. We make our case by comparing a pareto multiobjective ap-
proach against a single objective approach for solving single objective
global optimization problems in the absence of mutation. We show that
the fitness landscape is different in both cases and the multiobjective
approach scales faster and produces better solutions than the single ob-
jective approach.
Keywords: artificial evolution; diversity; multiobjective optimization.
1 Introduction
In biological systems, natural selection is carried out on multi-traits that are
usually in conflict. In real life animal breeding, a farmer selects those animals
to cull or mate based on a multi-trait selection index. Yet, in applying artificial
evolutionary systems to solve real life problems, researchers deviate from the
biological phenomena claiming that a biological characteristic may not be the
idle choice for a computational problem. Although this assertion contains some
truth, it
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