While it appears to be a good idea to use Genetic Algorithm(GA) to train a Neural network, past results do not c o n f i i such optimism. The main problems encountered are the speed of convergence, convergence to the wrong answer, and failure to converge. In this paper we combine GA and Simulated annealing to form a Genetic Boltzmann Machme(GBM) and attempt to understand the propertks of such a...

Process mining is helpful for deploying new business processes as well as auditing, analyzing and improving the already enacted ones. An improved pseudoparallel genetic algorithm is proposed with an asexual reproduction for avoiding crossover operators’ breach to nice gene patterns. The initial population is produced by greedy algorithm in order to enhance convergence velocity. Information exch...

in the present paper, we introduce some new sequence spaces derived by riesz mean and the notions of almost and strongly almost convergence in a real 2-normed space. some topological properties of these spaces are investigated. further, new concepts of statistical convergence which will be called weighted almost statistical convergence, almost statistical convergence and statistical convergence...

Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting...

The premature convergence of genetic algorithms (GAs) is the most major factor of slow evolution of GAs. In this paper we propose a novel method to solve this problem through competition of multiple offspring of individuals. Unlike existing methods, each parents in our method generates multiple offspring and then generated multiple offspring compete each other, finally winner offspring become t...

This paper presents a cumulative multi-niching genetic algorithm (CMN GA), designed to expedite optimization problems that have computationally-expensive multimodal objective functions. By never discarding individuals from the population, the CMN GA makes use of the information from every objective function evaluation as it explores the design space. A fitness-related population density control...

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population [3, 4, 8]. Maintaining an optimal level of diversity in the EA population is imperative to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Clearer understanding of the concept of population diversity, in the context of evolut...

This paper proposes a self-organizing genetic algorithm (SOGA) with good global search properties and a high convergence speed. First, we introduce a new dominant selection operator that enhances the action of the dominant individuals, along with a cyclical mutation operator that periodically varies the mutation probability in accordance with evolution generation found in biological evolutionar...

Unit Commitment (UC) is an important optimization task in the daily operation planning of the utilities. In mathematical terms, UC is a nonlinear optimization problem with a varied set of constraints. In recent years, Genetic Algorithm (GA), as a powerful tool to achieve global optima, has been successfully used for the solution of this complex optimization problem. Nevertheless, since the GA d...

A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the oth...