Optimization strategies gleaned from biological evolution

Several problems, in particular the ‘travelling salesman’ problem 1 wherein one seeks the shortest route encompassing a randomly distributed group of cities, have been optimized by repeated random alteration (mutation) of a trial solution followed by selection of the cheaper (fitter) solution. Most non-trivial problems have complicated fitness functions, and optimization tends to become stuck in local fitness maxima. A recently introduced strategy to escape (simulated annealing) involves accepting unfavourable mutations with finite probability 1–3 . Independently, there has been interest in genetic strategies which overcome the problem of fitness maxima in biological evolution 4–6 , and several authors have applied biological elements to optimization 7,8 . Here we use computer algorithms to investigate new strategies for the 64-city travelling salesman problem, which combine conventional optimization or ‘quenching’ with biological elements, namely having a population of trial solutions, helping weaker individuals to survive, and an analogue of sexual crossing-over of genes. The new strategies were faster and gave better results than simulated annealing.