Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
| Hi-index | 0.00 |
This paper describes an evolutionary algorithm (EA) which combines cellular automata, Cambrian explosions and massive extinction ideas in order to maintain diversity and automatically determine the population's size of the EA. Individuals are organized in a two-dimensional grid (2-dimensional cellular automaton surface) and are considered active or inactive according to the cellular automaton state. The individual state is updated according to the cellular automaton state rules at each step (iteration) of the evolutionary process. Only active individuals are subject to evolution by applying one of the genetic operators and considering just their active neighbors (when multiple parents are required). Depending on the total number of active individuals, a Cambrian explosion or a massive extinction operation is applied, in a random fashion. When the number of active individuals increases (decreases) the probability of applying a massive extinction increases (decreases) respectively. A symmetric rule is used for Cambrian explosions. Experiments on well known genetic algorithm testbed functions and on standard traveling salesman problems were conducted. Our experiments show that the proposed scheme is able to maintain diversity and find near optimal solutions in an appropriated number of fitness evaluations.