Comparison of Evolutionary Techniques Motivation EAs are general

Comparison of Evolutionary Techniques

Motivation • EAs are general problem solvers • EAs are themselves problems • EAs should be simpler to apply to new problems

Local Mating • Pick a location, L • Take a random walk from L, for each parent • Generate a new child at L

Local Survival • All sites, including L, are occupied • Each offspring must displace some individual, normally at L, or die

Complexity Analysis • Rank-based: O((m + l) * log(m + l)) – sorting dominates • Fitness-proportional: O(m + l) – linear, but generally poor results • Grid: O(l) – linear, and independent of m! 12 orders of magnitude faster than rankbased!

Ackley Function

Parallelization • Divide the population along each dimension of the grid • Exchange information only upon need • Asynchronous execution • No compromises necessary

Delta-Entropy Fitness Holes • • Define an entropy function Rank the population normally Calculate incremental entropy Randomly use in place of fitness

Floating-point Entropy • Given alleles {x 0…xn-1} • DH(i) = nearest neighbor to xi in {x 0…xi-1} • Disfavors clumps of alleles • Inhibits rampant breeding

Complexity Analysis • O((m + l)* log(m + l) * dim) for sorting each set of alleles • ~1 order of magnitude slower

Ackley Function