tailieunhanh - Evolutionary Robotics Part 8

Tham khảo tài liệu 'evolutionary robotics part 8', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 272 Frontiers in Evolutionary Robotics Three different statistic tests t-test Wilcoxon rank-sum and beta distribution were applied to discriminate the performance difference among a varying number of internal states. The beta distribution test has a good precision of significance test and its test result is similar to that of the Wilcoxon test. In many cases the beta distribution test of success rate was useful where the t-test could not discriminate the performance. The beta distribution test based on sampling theory has an advantage on analyzing the fitness distribution with even a small number of evolutionary runs and it has much potential for application as well as provide the computational effort. In addition the method can be applied to test the performance difference of an arbitrary pair of methodologies. The estimation of computational effort provides the information of an expected computing time for success or how many trials are required to obtain a solution. It can also be used to evaluate the efficiency of evolutionary algorithms with different computing time. We compared genetic programming approach and finite state machines and the significance test with success rate or computational effort shows that FSMs have more powerful representation to encode internal memory and produce more efficient controllers than the tree structure while the genetic programming code is easy to understand. 7. References . Angeline . Saunders and . Pollack 1994 . An evolutionary algorithm that constructs recurrent neural networks IEEE Trans. on Neural Networks 5 1 pp. 5465. . Angeline 1998 . Multiple interacting programs A representation for evolving complex Behaviors Cybernetics and Systems 29 8 pp. 779-806. D. Ashlock 1997 . GP-automata for dividing the dollar Genetic Programming 97 pp. 18-26. MIT Press D. Ashlock 1998 . ISAc lists a different representation for program induction Genetic Programming 98 pp. 3-10. Morgan Kauffman. B. Bakker and M. de Jong 2000 .

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