A simple real-coded extended compact genetic algorithm
6 April 2008Fossati, L., Lanzi, P. L., Sastry, K., Goldberg, D. E., Gomez, O. (2007). Proceedings of the 2007 IEEE Congress on Evolutionary Computation (CEC 2007). 342–348. [Full Paper].
Abstract:
This paper presents a simple real-coded estimation of distribution algorithm (EDA) design using ?-ary extended compact genetic algorithm (?ECGA) and discretization methods. Specifically, the real-valued decision variables are mapped to discrete symbols of user-specified cardinality using discretization methods. The ?ECGA is then used to build the probabilistic model and to sample a new population based on the probabilistic model. The effect of alphabet cardinality and the selection pressure on the scalability of the real-coded ECGA (rECGA) method is investigated. The results show that the population size required by rECGA—to successfully solve a class of additivelyseparable problems—scales sub-quadratically with problem size and the number of function evaluations scales sub-cubically with problem size. The proposed rECGA is simple, making it amenable for further empirical and theoretical analysis. Moreover, the probabilistic models built in the proposed realcoded ECGA are readily interpretable and can be easily visualized. The proposed algorithm and the results presented in this paper are first step towards conducting a systematic analysis of real-coded EDAs and towards developing a design theory for development of scalable and robust real-coded EDAs.
Related Posts:
- χ-ary extended compact genetic algorithm in C++
- On extended compact genetic algorithm
- χ-ary extended compact genetic algorithm for matlab in C++
- Real-coded ECGA for solving decomposable real-valued optimization problems
- Linkage learning in real-coded GAs with simplex crossover
No comments yet
