0000000000075501
AUTHOR
Jean-jacques Chabrier
Counting by Statistics on Search Trees: Application to Constraint Satisfaction Problems
In 1975, Knuth proposed a simple statistical method for investigating search trees. We use this technique for estimating the number of solutions of constraint satisfaction problem CSP and boolean satisfiability problem SAT instances. We show that, depending on domain reductions, tree-based estimates have a lower variance than estimates based on uniform sampling from the search space. Nevertheless, because the variance remains extremely high in the general case, a confidence interval cannot be derived, but a lower bound of the number of solutions. These results are confirmed by many experiments.
Measuring the Spatial Dispersion of Evolutionary Search Processes: Application to Walksat
In this paper, we propose a simple and efficient method for measuring the spatial dispersion of a set of points in a metric space. This method allows the quantifying of the population diversity in genetic algorithms. It can also be used to measure the spatial dispersion of any local search process during a specified time interval. We then use this method to study the way Walksat explores its search space, showing that the search for a solution often includes several stages of intensification and diversification.
Research of Complex Forms in Cellular Automata by Evolutionary Algorithms
This paper presents an evolutionary approach for the search for new complex cellular automata. Two evolutionary algorithms are used: the first one discovers rules supporting gliders and periodic patterns, and the second one discovers glider guns in cellular automata. An automaton allowing us to simulate AND and NOT gates is discovered. The results are a step toward the general simulation of Boolean circuits by this automaton and show that the evolutionary approach is a promising technic for searching for cellular automata that support universal computation.
Research of a Cellular Automaton Simulating Logic Gates by Evolutionary Algorithms
This paper presents a method of using genetic programming to seek new cellular automata that perform computational tasks. Two genetic algorithms are used : the first one discovers a rule supporting gliders and the second one modifies this rule in such a way that some components appear allowing it to simulate logic gates. The results show that the genetic programming is a promising tool for the search of cellular automata with specific behaviors, and thus can prove to be decisive for discovering new automata supporting universal computation.
A New Universal Cellular Automaton Discovered by Evolutionary Algorithms
In Twenty Problems in the Theory of Cellular Automata, Stephen Wolfram asks “how common computational universality and undecidability [are] in cellular automata.” This papers provides elements of answer, as it describes how another universal cellular automaton than the Game of Life (Life) was sought and found using evolutionary algorithms. This paper includes a demonstration that consists in showing that the presented R automaton can both implement any logic circuit (logic universality) and a simulation of Life (universality in the Turing sense).