EE4047_Part6

# EE4047_Part6 - Part 6: Advantages City University of Hong...

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City University of Hong Kong Part 6: Advantages Strength of GA ± Handle multi-modal problems ± Handle multi-objective problems ± Parallelism ± Handle constrainted problems

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Multi-objective Problems ± Linear combination ± Nonlinear combination ± Pareto-based approach Pareto Optimality feasible searching domain Pareto Optimal Set Example: Minimization of f 1 and f 2 f 2 f 1
Multi-objective ± Dominance : For an n-objective minimization problem, chromosome u is dominated by chromosome v ( v is preferable to u ) if f(v) is partially less than f(u) ( ) ( ) () () n j v f u f n i v f u f j j i i , , 2 , 1 and , , 2 , 1 L L = > = < u f v f p Example 5 3 3 2 x 2 5 4 3 2 x 1 f 4 ( · ) f 3 ( · ) f 2 ( · ) f 1 ( · ) ± Minimization Problem with 4-objectives ± x 1 is dominated by x 2 ± x 2 is more preferable than x 1

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Goldberg method ± All non-dominated individuals should have the same reproductive potential. A(1) D(1) C(1) B(1) E(2) F(2) G(3) f 1 f 2 Fonseca and Fleming Method ± The rank of each individual is defined by (1+p) where p is the number of individuals in the population that dominate it ± Non-dominated individuals are having the same rank, while dominated ones are penalized. F(3) G(4) f 1 f 2
Goldberg vs Fonseca/Fleming Individual Objective 1 Objective 2 Ranking Foncseca/ Fleming Goldberg 60.70(6) 90.68(2) 3 2 0.16(8) 26.76(4) 66.65(4) 15.94(5) 4 92.06(1) 90.76(1) 8 5 63.98(5) 0.38(8) 6 89.10(2) 9.59(6) 7 32.34(7) 4.89(7) 85.84(3) 38.78(3) ± Minimization Problem with 2-objectives ± Case 1: F(I a

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## This note was uploaded on 01/11/2011 for the course EE 4047 taught by Professor Kitsangtsang during the Fall '09 term at City University of Hong Kong.

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EE4047_Part6 - Part 6: Advantages City University of Hong...

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