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Solving the Inverse Kinematics Problem With Genetic Algorithms

This paper describes a method of utilizing genetic algorithms to search for the solution of an inverse kinematics problem in which there is a high degree of kinematic redundancy. A genetic algorithm is a search technique that attempts to simultaneously satisfy all system constraints while minimizing a given objective function. Various reordering operators are introduced which help the genetic algorithm effectively solve problems (such as this one) where changing one gene of an individual has an effect on the expression of one or more of the individual’s other genes. This gene interdependance is known as epistatis. Problems which contain a high degree of epistasis have been shown to be difficult to solve using genetic algorithms. In this case, genetic algorithms are applied using a simple encoding of the inverse kinematic problem into genetic algorithm space. Genetic algorithms with various traits are compared. Finally, the best performing genetic algorithm is compared to a reasonably well performing random search (applied to the same problem).