Showing papers on "Crossover published in 2009"

Influence of crossover on the behavior of Differential Evolution Algorithms

Abstract: In Differential Evolution Algorithms the crossover operator allows the construction of a new trial element starting from the current and mutant elements. Thus it controls which and how many components are mutated in each element of the current population. This work aims to analyze the impact the crossover operator and its parameter, the crossover rate, has on the behavior of Differential Evolution. The influence of the crossover rate on the distribution of the number of mutated components and on the probability for a component to be taken from the mutant vector (mutation probability) is theoretically analyzed for several variants of crossover, including classical binomial and exponential strategies. For each crossover variant the relationship between the crossover rate and the mutation probability is identified and its impact on the choice and adaptation of control parameters is analyzed theoretically and numerically. The numerical experiments illustrate the fact that the difference between binomial and exponential crossover variants is mainly due to different distributions of the number of mutated components. On the other hand, the behavior of exponential crossover variants was found to be more sensitive to the problem size than the behavior of variants based on binomial crossover.

Advancements in crossover theory

Abstract: Purpose – The central aim of this paper is to give an overview of theory and research on the crossover of (work‐related) wellbeing from employees to their partners at home. In addition, it seeks to discuss studies on the crossover of wellbeing from employees to their colleagues in the workplace. It aims to discuss possible moderators of the crossover effect and delineate a research agenda.Design/methodology/approach – The paper takes the form of a literature review.Findings – The review of the literature shows that strain may spillover from work to home, and consequently influence, the wellbeing of one's partner. Additionally, the paper discusses recent studies documenting that the enthusiasm for one's work may cross over to the partner as well. Furthermore, research has shown that employees influence one another in the workplace. Several conditions may facilitate such crossover, including the frequency of interactions, empathy, susceptibility to contagion, and similarity. The paper outlines a research ag...

Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique

Abstract: A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive performance with respect to some other state-of-the-art approaches in constrained evolutionary optimization.

Pairwise maximum entropy models for studying large biological systems: when they can work and when they can't.

Abstract: One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point.

A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems

Abstract: Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.

Pch2 links chromosome axis remodeling at future crossover sites and crossover distribution during yeast meiosis.

Abstract: Segregation of homologous chromosomes during meiosis I depends on appropriately positioned crossovers/chiasmata. Crossover assurance ensures at least one crossover per homolog pair, while interference reduces double crossovers. Here, we have investigated the interplay between chromosome axis morphogenesis and non-random crossover placement. We demonstrate that chromosome axes are structurally modified at future crossover sites as indicated by correspondence between crossover designation marker Zip3 and domains enriched for axis ensemble Hop1/Red1. This association is first detected at the zygotene stage, persists until double Holliday junction resolution, and is controlled by the conserved AAA+ ATPase Pch2. Pch2 further mediates crossover interference, although it is dispensable for crossover formation at normal levels. Thus, interference appears to be superimposed on underlying mechanisms of crossover formation. When recombination-initiating DSBs are reduced, Pch2 is also required for viable spore formation, consistent with further functions in chiasma formation. pch2Δ mutant defects in crossover interference and spore viability at reduced DSB levels are oppositely modulated by temperature, suggesting contributions of two separable pathways to crossover control. Roles of Pch2 in controlling both chromosome axis morphogenesis and crossover placement suggest linkage between these processes. Pch2 is proposed to reorganize chromosome axes into a tiling array of long-range crossover control modules, resulting in chiasma formation at minimum levels and with maximum spacing.

Multi-objective optimization using self-adaptive differential evolution algorithm

Abstract: In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

A Genetic Algorithm for Constructing Compact Binary Decision Trees

Abstract: Tree-based classifiers are important in pattern recognitio n and have been well studied. Although the problem of finding an optimal decision tree has r eceived attention, it is a hard optimization problem. Here we propose utilizing a genetic algorithm to improve on the finding of compact, near-optimal decision trees. We present a method to encode and decode a decision tree to and from a chromosome where genetic operators such as mutation and crossover can be applied. Theoretical properties of decisi on trees, encoded chromosomes, and fitness functions are presented.

Nonconvex Economic Dispatch With AC Constraints by a New Real Coded Genetic Algorithm

Abstract: In this paper, at first, a more realistic formulation of the economic dispatch (ED) problem is proposed, which considers more practical constraints and nonlinear characteristics than previous works in the area. The proposed ED formulation includes ramp rate limits, prohibited operating zones (POZs), system spinning reserve, valve loading effects, and multiple fuel options, which usually are found simultaneously in realistic power systems. In the next stage, security constraints of the power system are also included in the proposed model, which leads to nonconvex ED problem with AC constraints. To solve this problem a new real-coded genetic algorithm (RCGA) with arithmetic-average-bound crossover (AABX) and hybrid mutation (HM) is presented. HM of the proposed RCGA is composed of wavelet and Michalewicz mutations. The effectiveness of the proposed RCGA to solve both the ED and ED with AC constraints is shown on different test systems and compared with some of the most recently published research works in the area.

A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment

Abstract: Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback of the filter. By using genetic operators, the premature convergence of the particles is avoided and the search region of particles enlarged. The GA-inspired proposal distribution is proposed and the corresponding importance weight is derived to approximate the given target distribution. Finally, a computer simulation is performed to show the effectiveness of the proposed method.

Analysis of diversity-preserving mechanisms for global exploration*

Abstract: Maintaining diversity is important for the performance of evolutionary algorithms. Diversity-preserving mechanisms can enhance global exploration of the search space and enable crossover to find dissimilar individuals for recombination. We focus on the global exploration capabilities of mutation-based algorithms. Using a simple bimodal test function and rigorous runtime analyses, we compare well-known diversity-preserving mechanisms like deterministic crowding, fitness sharing, and others with a plain algorithm without diversification. We show that diversification is necessary for global exploration, but not all mechanisms succeed in finding both optima efficiently. Our theoretical results are accompanied by additional experiments for different population sizes.

Approximating geometric crossover in semantic space

Abstract: We propose a crossover operator that works with genetic programming trees and is approximately geometric crossover in the semantic space. By defining semantic as program's evaluation profile with respect to a set of fitness cases and constraining to a specific class of metric-based fitness functions, we cause the fitness landscape in the semantic space to have perfect fitness-distance correlation. The proposed approximately geometric semantic crossover exploits this property of the semantic fitness landscape by an appropriate sampling. We demonstrate also how the proposed method may be conveniently combined with hill climbing. We discuss the properties of the methods, and describe an extensive computational experiment concerning logical function synthesis and symbolic regression.

A hybrid genetic algorithm that optimizes capacitated vehicle routing problems

Abstract: This study primarily focuses on solving a capacitated vehicle routing problem (CVRP) by applying a novel hybrid genetic algorithm (HGA) capable of practical use for manufacturers. The proposed HGA has three stages. First, the nearest addition method (NAM) was incorporated into sweep algorithm (SA) that simultaneously accounts for axial and radius relationships among distribution points with the depot to generate a well-structured initial chromosome population, rather than adopting either the NAM OR SA alone. Second, response surface methodology (RSM) was employed to optimize crossover probability and mutation probability via systematic experiments. Finally, an improved sweep algorithm was incorporated into the GA, producing a stir over gene permutations in chromosomes that enhance the exploration diversity of the GA, thereby avoiding convergence in a limited region, and enhancing the search capability of the GA in approaching a close-to-optimal solution. Furthermore, an elitism conservation strategy holding superior chromosomes to replace inferior chromosomes was also performed. As the proposed HGA is primarily used to solve practical problem, benchmark problems with fewer than 100 distribution points from an Internet website were utilized to confirm the effectiveness of the proposed HGA. A real case regarding the mission of local active distribution from armed forces in Taiwan details the analytical process and demonstrates the practicability of the proposed HGA to optimize the CVRP.

Economic dispatch using an efficient real-coded genetic algorithm

Abstract: The authors present a new formulation of the economic dispatch (ED) problem considering more practical constraints and nonlinear characteristics than previous works in the area. The proposed formulation includes ramp rate limits, prohibited operating zones, system spinning reserve, valve loading effects, multiple fuel options, which usually be found simultaneously in realistic power systems. To solve the ED formulation, an efficient real-coded genetic algorithm (RCGA) with arithmetic-average-bound crossover and wavelet mutation is presented. To show the effectiveness of the solution method, it is applied to five test systems having non-convex solution spaces and compared with some of the most recently published approaches. The obtained results reveal the performance of the proposed RCGA.

Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression

Abstract: In this paper, we apply the ideas from [2] to investigate the effect of some semantic based guidance to the crossover operator of GP. We conduct a series of experiments on a family of real-valued symbolic regression problems, examining four different semantic aware crossover operators. One operator considers the semantics of the exchanged subtrees, while the other compares the semantics of the child trees to their parents. Two control operators are adopted which reverse the logic of the semantic equivalence test. The results show that on the family of test problems examined, the (approximate) semantic aware crossover operators can provide performance advantages over the standard subtree crossover adopted in Genetic Programming.

Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral - derivative controller in automatic voltage regulator system

Abstract: Optimal tuning of proportional-integral-derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.

On stability and convergence of the population-dynamics in differential evolution

Abstract: Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to understand the search behavior of evolutionary algorithms and to develop more efficient algorithms. In this paper we investigate the dynamics of a canonical Differential Evolution (DE) algorithm with DE/rand/1 type mutation and binomial crossover. Differential Evolution (DE) is well known as a simple and efficient algorithm for global optimization over continuous spaces. Since its inception in 1995, DE has been finding many important applications in real-world optimization problems from diverse domains of science and engineering. The paper proposes a simple mathematical model of the underlying evolutionary dynamics of a one-dimensional DE-population. The model shows that the fundamental dynamics of each search-agent (parameter vector) in DE employs the gradient-descent type search strategy (although it uses no analytical expression for the gradient itself), with a learning rate parameter that depends on control parameters like scale factor F and crossover rate CR of DE. The stability and convergence-behavior of the proposed dynamics is analyzed in the light of Lyapunov's stability theorems very near to the isolated equilibrium points during the final stages of the search. Empirical studies over simple objective functions are conducted in order to validate the theoretical analysis.