. This study focuses on selective crossover, which is an adaptive recombination operator. We propose three alternative encodings for the Royal Road problem. We use these encodings to analyse the performance of selective crossover with respect to different encodings. This study shows that the performance

Abstract—Research into adjusting the probabilities of crossover and mutation � � in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. � � and � � greatly determine whether the algorithm will find a near-optimum solution or whether it will find

, the crossover hill-climbing exploits the selfadaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities

The dynamics of a genetic algorithm undergoing ranking selection, mutation, and two-point crossover for the ones-counting problem is studied using a statistical mechanics approach. This approach has been used previously to study this problem, but with uniform crossover. Two-point crossover induces

In this paper we report the results of experiments on multi-parent reproduction in an adaptive genetic algorithm framework. An adaptive mechanism based on competing subpopulations is incorporated into the algorithm in order to detect the best crossovers. Experiments on a number of test functions

Many data mining tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as well as in clustering and schema mapping algorithms. Because the number of object pairs grows quadratically with the size

Abstract—Several adaptive variants of differential evolution are described and compared in two sets of benchmark problems. The influence of exponential crossover on efficiency of the search is studied. The use of both types of crossover together makes the algorithms more robust. Such algorithms

The process of identifying record pairs that represent the same real-world entity in multiple databases, commonly known as record linkage, is one of the important initial steps in many data mining applications. Record linkage of millions of records is a computationally expensive task. Various

and then performs all clustering operations on this obtained grid structure. To cluster efficiently and simultaneously, to reduce the influences of the size of the cells and inherits the advantage with the low time complexity, an Adaptive Crossover-Imaged Clustering Algorithm, called ACICA, is proposed

allowed us to define a fitness function, and two operators arc-crossover and arc-mutation. We introduce here a new operator called Self-Adap-arc which uses the idea of self adaptivity and looks at the constraint network during the evolution. This operator is used to improve the stochastic search.