Coevolutionary genetic algorithm for constraint satisfaction with a genetic repair operator for effective schemata formation

We discuss a coevolutionary genetic algorithm for constraint satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our coevolutionary genetic algorithm (CGA) consists of two GA populations; the first GA, called “H-GA”, searches for the solutions in a given environment (problem), and the second GA, called “P-GA”, searches for effective genetic information involved in the H-GA, namely, good schemata. Thus, each individual in P-GA consists of alleles in H-GA or “don't care” symbol representing a schema in the H-GA. These GA populations separately evolve in each genetic space at different abstraction levels and affect with each other by two genetic operators: “superposition” and “transcription”. We then applied our CGA to constraint satisfaction problems (CSPs) incorporating a new stochastic “repair” operator for P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. We carried out two experiments: First, we examined the performance of CGA on various “general” CSPs that are generated randomly for a wide variety of “density” and “tightness” of constraint conditions in the CSPs that are the basic measures of characterizing CSPs. Next, we examined “structured” CSPs involving latent “cluster” structures among the variables in the CSPs. For these experiments, computer simulations confirmed us the effectiveness of our CGA</p

Analyzing the behavior of a Virtual Social Networking in an online social game based on a Serious Game

This paper develops a hybrid algorithm that combines evolutionary programming to improve the optimization of a virtual social networking in the Facebook's social network game named “My Tribe”. This prototype takes a collection of individuals and interprets the expectedly of collective studies in different activities. Using a specific model to create and verify the method, led by means of a collection of structure heuristics acquired from the prospect of reaching the ideal tribe, this prototype can operate in two ways: by generating a tribe without restrictions or by a creation of a tribe accordant to one of the three preset architecture of the tribe. Seven construction heuristics are taken into account, through different combinations of two batches of the creation of the initial population, the first generated from an established combination, one popularly used in anthropology and the other using the psychosocial analysis of habitat patterns. The goal is to make sure the relative importance of the initial population size tribe and the construction of a Heuristic based on the general acceptability of the resulting tribe, which is validated with the Cultural Algorithm