An empirical study of crossover and mass extinction in a genetic algorithm for pathfinding in a continuous environment

Genetic algorithms are an often used tool for the problem of pathfinding. Their ability to find good solutions to multiobjective optimization problems makes them well suited to this task. Of course, genetic algorithms embody a broad range of techniques and strategies including crossover, mutation and mass extinction, each with multiple parameters and implementations. In this paper, we examine the effects of crossover and mass extinction on a genetic algorithm for planning a path through known obstacles in an unconstrained, continuous, static environment. Using a mutation-only genetic algorithm as a baseline, we study the effect of crossover with and without mass extinction events that occur at several different probabilities. We find that while both offer sometimes significant improvement in some cases, neither is universally beneficial.

[1]  Heng-Ming Tai,et al.  Autonomous local path planning for a mobile robot using a genetic algorithm , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[2]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[3]  Kalyanmoy Deb,et al.  Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms , 2012, Soft Computing.

[4]  Hu Jun,et al.  Multi-objective Mobile Robot Path Planning Based on Improved Genetic Algorithm , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[5]  Carlos M. Fonseca,et al.  Multiobjective genetic algorithms , 1993 .

[6]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach , 2014, IEEE Transactions on Evolutionary Computation.

[7]  Gary G. Yen,et al.  Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation , 2003, IEEE Trans. Evol. Comput..

[8]  Ole J Mengshoel,et al.  The Crowding Approach to Niching in Genetic Algorithms , 2008, Evolutionary Computation.

[9]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[10]  H. David Mathias,et al.  Micro Aerial Vehicle Path Planning and Flight with a Multi-objective Genetic Algorithm , 2016 .

[11]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[12]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[13]  Prashanth Tonupunuri.  Evolutionary based path-finding for mobile agents in sensor networks , 2008 .

[14]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[15]  Changwen Zheng,et al.  Coevolving and cooperating path planner for multiple unmanned air vehicles , 2004, Eng. Appl. Artif. Intell..

[16]  Peter J. Fleming,et al.  Multiobjective Genetic Programming: A Nonlinear System Identification Application , 1997 .

[17]  S. F.  Memory-efficient Genetic Algorithm for Path Optimization in Embedded Systems , 2013 .

[18]  Aimin Zhou,et al.  A Multiobjective Evolutionary Algorithm Based on Decomposition and Preselection , 2015, BIC-TA.

[19]  R. Miikkulainen,et al.  Extinction Events Can Accelerate Evolution , 2015, PloS one.

[20]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[21]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[22]  T. Manikas,et al.  AUTONOMOUS ROBOT NAVIGATION USING A GENETIC ALGORITHM WITH AN EFFICIENT GENOTYPE STRUCTURE , 2004 .

[23]  Ralf Salomon,et al.  Implementation of Path Planning using Genetic Algorithms on Mobile Robots , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[24]  Ł. Kuczkowski,et al.  Extinction Event Concepts for the Evolutionary Algorithms , 2012 .