Abstract:
In this paper we report on the effect of viral infection with tropism on the formation of building blocks in genetic operations. In previous research, we applied genetic algorithms to the analysis of time-series signals with noise. We demonstrated the possibility of reducing the number of required entities and improving the rate of convergence when searching for a solution by having some of the host chromosomes harbor viruses with a tropism function. Here, we simulate problems having both multimodality and deceptiveness features and problems that include noise as test functions, and show that viral infection with tropism can increase the proportion of building blocks in the population when it cannot be assumed that a necessary and sufficient number of entities are available to find a solution. We show that this capability is especially noticeable in problems that include noise.

Abstract:
This report provides documentation for the general purpose genetic algorithm toolbox for matlab in C++. The fitness function used in the toolbox is written in matlab. The toolbox provides different selection, recombination, mutation, niching, and constraint-handling operators. Problems with single and multiple objectives can be solved with the toolbox. Moreover, the toolbox is easily extensible and customizable for incorporating other operators and for solving user-defined search problems.

Abstract:
This report provides documentation for the general purpose genetic algorithm toolbox. The toolbox provides different selection, recombination, mutation, niching, and constraint-handling operators. Problems with single and multiple objectives can be solved with the toolbox. Moreover, the toolbox is easily extensible and customizable for incorporating other operators and for solving user-defined search problems.

Abstract:
Excited-state photodynamics is important in numerous varieties of important materials applications (e.g., liquid crystal display, light emitting diode), pharmaceuticals, and chemical manufacturing processing. We study the effectiveness of multiobjective genetic and evolutionary algorithms in multiscaling excited-state direct photodynamics via rapid reparameterization of semiempirical methods. Using a very limited set of ab initio and experimental data, semiempirical parameters are reoptimized to provide globally accurate potential energy surfaces, thereby eliminating the need for expensive ab initio dynamics simulations. Through reoptimization, excited-state energetics are predicted accurately via semiempirical methods, while retaining accurate ground-state predictions. In our initial study of small photo-excited molecules, our results show that the multiobjective evolutionary algorithm consistently yields solutions that are significantly better – up to 384% lower error in the energy and 87% lower error in the energy-gradient – than those reported previously. As verified with direct quantum dynamical calculations, multiple high-quality parameter sets obtained via genetic algorithms show near-ideal behavior on critical and untested excited-state geometries. The results demonstrate that the reparameterization via evolutionary algorithms is a promising way to extend direct dynamics simulations of photochemistry to multi-picosecond time scales and to larger molecules, with promise in more application beyond simple molecular chemistry.

Abstract:
This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey.

Abstract:
This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifically, additively decomposable problems of order k whose subsolution fitnesses are sampled from the standard uniform distribution U[0,1] are analyzed. The scalability of the selectorecombinative GA is investigated for 10,000 rADP instances. The validity of facetwise models in bounding the population size, run duration, and the number of function evaluations required to successfully solve the problems is also verified. Finally, rADP instances that are easiest and most difficult are also investigated.