This paper continues the development, begun in Part I, of the relationship between the simple genetic algorithm and the Walsh transform. The mixing scheme (comprised of crossover and mutation) is essentially "triangularized" when expressed in terms of the Walsh basis. This leads to a formulation of the inverse of the expected next generation operator. The fixed points of the mixing scheme are a...

This paper proposes a blade sorting method based on the cloud adaptive genetic algorithm (CAGA), which is used to optimize unbalanced of asymmetric rotor aero-engine. Firstly, by analyzing unbalance arrangement caused deviation mass moment blade, and considering concentricity disk, an optimization model amount assembly was established. Secondly, selection operator, crossover mutation operator w...

The shortcoming of the standard genetic algorithm is analysed. An improved genetic algorithm with modified mutation operator and adaptive probabilities of crossover and mutation is proposed. Simulation experiments have been carried and the results show that the modifications are very effective. In this paper, an optimum charge plan for steelmaking continuous casting production scheduling is als...

The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal values and therefore cannot converge to global optima solution. In this paper, we introduce several new variation operators for the proposed hybrid genetic algorithm for the clus...

AbstractUnderstanding operator bias in evolutionary computation is important because it is possible for the operator’s biases to work against the intended biases induced by the fitness function. In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length...

Standard Evolutionary processes can be understood as a stochastic process p = G p which propagates a parent population p at generation t to a parent population p via a stochastic operator G : Λ → Λ. Here, populations p ∈ Λ are distributions over a search space (or genotype space) G, and Λ is the space (or simplex) of such distributions. We address the details of the formalism and how G is given...

Traveling Salesman Problem (TSP) is an NP-hard Problem, which has many different real life applications. Genetic Algorithms (GA) are robust and probabilistic search algorithms based on the mechanics of natural selection and survival of the fittest that is used to solve optimization and many real life problems. This paper presents Genetic Algorithm for TSP. Moreover it also shows best suitable p...

An improved genetic algorithm for the dynamic layout problem, based on a paper that was published in this journal, is formulated and tested in this research. Our genetic algorithm differs from the existing implementation in three ways: first, we adopt a different crossover operator, second, we use mutation, and third, we use a new generational replacement strategy to help increase population di...

The work presented here is intended as an evolutionary task-specific module for referring expression generation and aggregation to be enclosed in a generic flexible architecture. Appearances of concepts are considered as genes, each one encoding the type of reference used. Three genetic operators are used: classic crossover and mutation, plus a specific operator dealing with aggregation. Fitnes...

In this paper, we have proposed a new genetic algorithm for p-median location problem. In this regard, prior genetic algorithms were designed for p-median location problem by proposing several methods that are used in generation of initial population, crossover and mutation operators, and new operator socalled re-allocation has been incorporated into the algorithm that causes to find the optima...