Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a Pareto converging genetic algorithm

Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems. We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort

Robot path planning using a genetic algorithm

Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions