Evolutionary genetic algorithms in a constraint satisfaction problem: Puzzle Eternity II

Proceeding of: International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, June 10-12, 2009This paper evaluates a genetic algorithm and a multiobjective evolutionary algorithm in a constraint satisfaction problem (CSP). The problem that has been chosen is the Eternity II puzzle (E2), an edge-matching puzzle. The objective is to analyze the results and the convergence of both algorithms in a problem that is not purely multiobjective but that can be split into multiple related objectives. For the genetic algorithm two different fitness functions will be used, the first one as the score of the puzzle and the second one as a combination of the multiobjective algorithm objectives.This work was supported in part by the Carlos III University of Madrid under grant PIF UC3M01-0809 and by the Ministry of Science and Innovation under project TRA2007-67374-C02-02

Cellular memetic algorithms

This work is focussed on the development and analysis of a new class of algorithms, called cellular memetic algorithms (cMAs), which will be evaluated here on the satisfiability problem (SAT). For describing a cMA, we study the effects of adding specific knowledge of the problem to the fitness function, the crossover and mutation operators, and to the local search step in a canonical cellular genetic algorithm (cGA). Hence, the proposed cMAs are the result of including these hybridization techniques in different structural ways into a canonical cGA. We conclude that the performance of the cGA is largely improved by these hybrid extensions. The accuracy and efficiency of the resulting cMAs are even better than those of the best existing heuristics for SAT in many cases.Facultad de Informátic

A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compare the effectiveness of a symbolic solver (CVC3), a random solver, three hybrid solvers (i.e., mix of random and symbolic), and two heuristic search solvers. We evaluate the solvers on two benchmarks: one consisting of manually generated constraints and another generated with a concolic execution of 8 subjects. In addition to fully decidable constraints, the benchmarks include constraints with non-linear integer arithmetic, integer modulo and division, bitwise arithmetic, and floating-point arithmetic. As expected symbolic solving (in particular, CVC3) subsumes the other solvers for the concolic execution of subjects that only generate decidable constraints. For the remaining subjects the solvers are complementary

Genetic algorithm against cancer,”

Abstract. We present an evolutionary approach to the search for effective vaccination schedules using mathematical computerized model as a fitness evaluator. Our study is based on our previous model that simulates the Cancer -Immune System competition activated by a tumor vaccine. The model reproduces pre-clinical results obtained for an immunoprevention cancer vaccine (Triplex) for mammary carcinoma on HER-2/neu mice. A complete prevention of mammary carcinoma was obtained in vivo using a Chronic vaccination schedule. Our genetic algorithm found complete immunoprevention with a much lighter vaccination schedule. The number of injections required is roughly one third of those used in Chronic schedule

Algoritmos evolutivos : Estudio de escalabilidad sobre el problema 3-SAT

En este trabajo se investiga la influencia del tamaño del problema en la performance de los Algoritmos Evolutivos (AEs), utilizados para resolver un conocido problema NP-completo: el 3-SAT. Para ello se ha realizado una amplia recopilación de diferentes variantes de AEs, desarrollados para mejorar la calidad de los resultados. También, se describen los AEs usados para analizar el comportamiento de los mismos frente a problemas de tamaño creciente, tomados de benchmarks internacionales. Este estudio también incluye el análisis de la incorporación de distribución y paralelismo en la resolución del problema, como caminos alternativos para resolverlo en menor tiempo.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI

Algoritmos evolutivos : Estudio de escalabilidad sobre el problema 3-SAT

En este trabajo se investiga la influencia del tamaño del problema en la performance de los Algoritmos Evolutivos (AEs), utilizados para resolver un conocido problema NP-completo: el 3-SAT. Para ello se ha realizado una amplia recopilación de diferentes variantes de AEs, desarrollados para mejorar la calidad de los resultados. También, se describen los AEs usados para analizar el comportamiento de los mismos frente a problemas de tamaño creciente, tomados de benchmarks internacionales. Este estudio también incluye el análisis de la incorporación de distribución y paralelismo en la resolución del problema, como caminos alternativos para resolverlo en menor tiempo.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI

Algoritmos evolutivos : Estudio de escalabilidad sobre el problema COUNTSAT

En este trabajo se investiga la influencia del tamaño del problema en la performance de los Algoritmos Evolutivos (AEs), utilizados para resolver una variante NP-difícil del problema MAXSAT denominada COUNTSAT. Para ello se ha realizado una recopilación de diferentes tipos de AEs, desarrollados para mejorar la calidad de los resultados. También, se describen los AEs usados para analizar el comportamiento de los mismos frente a problemas de tamaño creciente, tomados de benchmarks internacionales. Este estudio incluye el análisis de la incorporación de distribución y paralelismo en la resolución del problema, como caminos alternativos para encontrar el óptimo con menor esfuerzo numérico.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Genetic optimisations for satisfiability and Ramsey theory

The art of using evolutionary mechanisms for identifying satisfiability has produced a range of efficient solutions to this otherwise computationally challenging problem. Since their first use these evolutionary methods have been changed and adapted to produce increasingly efficient solutions. This paper introduces two unique alternatives to the optimisation of these methods, the first through the introduction of alternative mutation operators and the second through utilizing a grammatical encoding which has been proven to improve neuroevolution. The goal of this paper is to identify whether these two alternatives are candidates for future investigation in improving evolutionary satisfiability solvers