Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A new kind of science
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mechanisms of Emergent Computation in Cellular Automata
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolution of Asynchronous Cellular Automata for the Density Task
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Genetic Algorithm Discovers Particle-Based Computation in Cellular Automata
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Asynchronous evolutionary search: multi-population collaboration and complex dynamics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
| Hi-index | 0.00 |
Efficient recombination and selection strategies in evolutionary search models have a great impact on the quality of detected solutions. Evolving populations of adaptive individuals can potentiallly trigger important results for the design of evolutionary models. The Geometric Collaborative Evolutionary (GCE) model takes this approach by integrating agent-based features (such as autonomy and communication) into the evolving population. Each individual is able to act like an agent in the sense that communication with other individuals is possible and facilitates the selection of a mate for recombination. The contribution of this paper is twofold: (i) the benefits of GCE having an agent-inspired component are assessed in a set of numerical experiments for the optimization of difficult real-valued functions, and (ii) the GCE algorithm is applied with successful results for solving the density classification problem in one dimensional binary state Cellular Automata (CA). The GCE model clearly benefits from its agent-inspired component obtaining better numerical results compared to its GCE variant with no agent-inspired behavior. The organization of population in dynamic societies with different strategies for recombination plays an important role in the search process. Furthermore, numerical results and comparisons emphasize a better performance of the GCE model in evolving CA rules compared to other evolutionary models.