Lecture 6. Niching and Speciation (3)

1. Lecture 6. Niching and Speciation (3) 학습목표 진화로 얻어진 해의 다양성을 확보하기 위한 대표적인 방법에 대해 이해한다.

2. Outline • Review of the last lecture • A motivating example about niching • Different niching methods • Fitness sharing • Continue with last lecture’s topics on sharing • Crowding • Deterministic crowding • Speciation and mating restriction • Summary

3. What Is Crowding • Crowding techniques insert new elements into the population by replacing similar elements. It’s about replacement strategies • Crowding techniques strive to maintain the preexisting diversity of a population • Crowding methods do not modify fitness

4. Crowding: Early Days • Cavicchio’s preselection scheme: • Purpose: prevent premature convergence, preserve diversity • What it is: an offspring (usually the fitter one) replaces the worse parent • How well it works for niching: poorly, unable to form niches • De Jong’s Crowding (crowding factor model): • Purpose: preserve population diversity • What it is: restricted replacement, offspring replaces the most similar parent in a same • How well it works for niching: poorly, only finding two peaks • Common feature: both involve modification of replacement strategies. A restriction is often imposed

5. Deterministic Crowding: Algorithm(1) • Problems with De Jong’s crowding: high replacement error (i.e., replacing an individual of one class by another one from a different class) • Mahfoud’s deterministic crowding algorithm: Input: g – number of generations to run the algorithm m – population size Output: P(g) – final population Algorithm: • P(c)  initialize () • FOR t  1 to g DO • P(t)  shuffle (P(t-1)) • FOR i  0 TO m/2 – 1 DO • DCA () • OD • OD

6. Deterministic Crowding: Algorithm(2) DCA (): P1  a2i+1(t) P2  a2i+2(t) {C1, C2}  recombine (P1, P2) C1’  mutate (C1) C2’  mutate (C2) IF [d(P1, C1’)+d(P2, C2’)] <= [d(P1, C2’)+d(P2, C1’)] THEN IF f(C1’) > f(P1) THEN a2i+1(t)  C1’ FI IF f(C2’) > f(P2) THEN a2i+2(t)  C2’ FI ELSE IF f(C2’) > f(P1) THEN a2i+1(t)  C2’ FI IF f(C1’) > f(P2) THEN a2i+2(t)  C1’ FI FI

7. Deterministic Crowding: Discussion • Capable of niching, i.e., locating and maintaining multiple peaks • Minimal replacement error • Few parameters to tune • Fast, no distance calculations needed • Population size is important. Should be large if unsure • Should use full crossover, i.e. crossover rate = 1.0 • The following appears to all crowding methods: • Unlike sharing methods, crowding methods do not allocate individuals proportional to peak fitness. Instead, the number of individuals congregating about a peak is largely determined by the size of that peak’s basin of attraction under crossover • Similarity can be measured at either genotypic or phenotypic level

8. Speciation (in a narrow sense) • In a broad sense, niching and speciation can be regarded as the same thing • In a narrow sense, the main emphasis of niching is on distributing individuals among different peaks, not finding exact optima efficiently. Speciation focuses on search within each peak • A speciation method restricts mating to similar individuals and discourages mating of individuals from different peaks (species) • In order to apply speciation techniques properly, individuals representing each peak must be found first. Speciation techniques cannot be used independently • Niching and speciation are complementary • Similarity can be measured at either genotypic or phenotypic level

9. Mating Restriction • Use tags: each individual consists of a tag and a functional string • Use distance: either in the genotypic or phenotypic space template tag functional string #1#0 1010 1010 …… 101 • Tag participate in (crossover and) mutation, but not fitness evaluation • Templates can also be used • Has been shown to be effective for multimodal function optimization • Only individuals with the same tag are allowed to mate Define a threshold parameter smate -Two individuals are allowed to mate only when their distance is smaller than smate -GAs with niching and mating restriction were found to better distribute the population across the peaks than GAs with sharing alone Mating restriction always applies during recombination