In this paper, echo cancellation is done using genetic algorithm (GA). The genetic algorithm is implemented by two kinds of crossovers; heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and diff...

In this work we present an application of the Confidence Interval Based Crossover using L2 Norm (CIXL2) and BLX-α crossovers to the evolution of neural networks. CIXL2 is a new crossover operator, based on obtaining the statistical features of the best individuals of the population. These features are used as virtual parents for the crossover operator. Due to the permutation problem of neural n...

Crossovers produced by homologous recombination promote accurate chromosome segregation in meiosis and are controlled such that at least one forms per chromosome pair and multiple crossovers are widely spaced. Recombination initiates with an excess number of double-strand breaks made by Spo11 protein. Thus, crossover control involves a decision by which some breaks give crossovers while others ...

Aircraft arrival sequencing and scheduling (ASS) is a major issue in the daily air traffic control (ATC) operations. This paper reports on the application of genetic algorithms (GAs) to tackle the ASS problem in multi-runway systems. Most existing GAs for ASS are confronted with feasibility and efficiency problems in the design of their evolutionary operators, particularly the crossover. The ne...

Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Despite theoretical analysis, however, it appears difficul...

Recombination (also called crossover) operators are widely used in EAs to generate offspring solutions. Although the usefulness of recombination has been well recognized, theoretical analysis on recombination operators remains a hard problem due to the irregularity of the operators and their complicated interactions to mutation operators. In this paper, as a step towards analyzing recombination...

Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperpl...

Genetic algorithms (GAs) generate solutions to optimization problems using techniques inspired by natural evolution, like crossover, selection and mutation. In that process, crossover operator plays an important role as an analogue to reproduction in biological sense. During the last decades, a number of different crossover operators have been successfully designed. However, systematic comparis...

ABSTRACT In this paper we study and compare the search properties of different crossover operators in genetic programming (GP) using probabilistic models and experiments to assess the amount of genetic material exchanged between the parents to generate the offspring. These operators are: standard crossover, onepoint crossover and a new operator, uniform crossover. Our analysis suggests that sta...