UNDERSTANDING GENETIC ALGORITHM DYNAMICS USING HARVESTING STRATEGIES

The genetic algorithm (GA) finds optimal solutions over complex fitnes s landscapes using a method developed in analogy to genetic laws and n atural selection. The method essentially operates by optimizing the tr adeoff between exploring new points in the search space and exploiting previous information discovered thus far. In this tradeoff, an unders tanding of the internal GA dynamics, how exactly the GA arrives at an optimum solution, remains somewhat mysterious. Harvesting strategies a re introduced here to parameterize the GA's dynamical behavior of elev ating sub-threshold solutions toward optimum. The method of harvesting balances the competing aims of population diversity counterweighted a gainst rapid convergence toward the optimum solution. The work establi shes that: (1) an upper bound on the fitness ratio exists, above which harvesting becomes too disruptive to the population diversity; (2) an alytical conditions for considering elevation within the genetic algor ithm are a specific case of logistic growth; and (3) explicit relation s exist for the maximum yield and maximum harvestable fraction for 2-s tage, 3-stage and finally n-stage harvesting strategies as a function of fitness ratio. Simple expressions for GA time complexity between ha rvesting steps are presented.