Beginning with the early days of the genetic algorithm and the schema theorem it has often been argued that the crossover operator is the more important genetic operator. The early Royal Road functions were put forth as an example where crossover would excel, yet mutation based EAs were subsequently shown to experimentally outperform GAs with crossover on these functions. Recently several new R...

We present an analysis of the performance of an elitist Evolutionary algorithm using a recombination operator known as 1-Bit-Swap on the Royal Roads test function based on a population. We derive complete, approximate and asymptotic convergence rates for the algorithm. The complete model shows the benefit of the size of the population and recombination pool.

We present a number of bounds on convergence time for two elitist population-based Evolutionary Algorithms using a recombination operator k-Bit-Swap and a mainstream Randomized Local Search algorithm. We study the effect of distribution of elite species and population size.

We consider the NP-hard no-wait flowshop scheduling problem with sequence-dependent setup times to minimize makespan. We propose a hybrid heuristic which combines the iterated greedy process with local search method based on a block swap operator. Experimental results show that the proposed heuristic is comparatively more effective than the existing metaheuristics.

In this paper, we prove the existence of a random fixed point of by using pointwise asymptotically nonexpansive random operator and the stability resultsof two iterative schemes for random operator.

In this paper, we propose a solution method for the Generalized Traveling Salesman Problem (GTSP) based on a Memetic Algorithm (Genetic Algorithm plus Local Search, see, e.g., [7]). The GTSP is a generalization of the well-known Traveling Salesman Problem (TSP). The main contribution of the paper stands in the crossover operator based on the exploration of a large neighborhood around the father...

This paper puts forward an adaptive genetic algorithm to solve the multi-group homogenization in the solution space. The use of good-point set approach improves the initial population, ensuring them a uniform distribution in the solution space. In the evolution, each population implements independent genetic operations (selection, good-point set crossover, and mutation). The introduction of ada...

The Commonality-Based Crossover Framework has been presented as a general model for designing problem specific operators. Following this model, the Common Features/Random Sample Climbing operator has been developed for feature subset selection--a binary string optimization problem. Although this problem should be an ideal application for genetic algorithms with standard crossover operators, exp...

Operator-probability adaptation in a genetic-algorithm/ heuristic hybrid for minimum cost routing and wavelength allocation of multi-wavelength all-optical transport networks is described. The hybrid algorithm uses an object-oriented representation of networks, and incorporates four operators: path mutation, single-point crossover, reroute and shift-out. The adaptation algorithm is based on tha...

The recent popularity of genetic algorithms (GA’s) and their application to a wide range of problems is a result of their ease of implementation and flexibility. Evolutionary techniques are often applied to optimization and related search problems where the fitness of a possible result is easily established. Although many landscapes are searched quickly, there are those, such as permutation lan...