Genetic Algorithms In Dynamic Environments Research Articles

On the applicability of diploid genetic algorithms in dynamic environments

Diploidity is the essence of the nature. However, it has largely been ignored by the computer science fraternity. Simple Genetic Algorithms and their variants have extensively been used in solving NP hard problems in-spite of the fact that Diploid Genetic Algorithms assure robustness as against Simple Genetic Algorithms which solitary guarantee optimization. Moreover, the past endeavors proved that these algorithms are more successful in dynamic environments as compared to their haploid counterpart. The work proves the above point by applying Diploid genetic Algorithms to Dynamic Travelling Salesman Problem and comparing the results to Greedy Approach and Simple Genetic Algorithms. The work also presents a hybrid approach namely Greedy Genetic Approach. The results of the experiments proved the fact that diploidity ensures robustness. In the experiments carried out, the three variants of dominance were implemented and 115 trials bought forth the point that though Haploid and Greedy Approaches do not outperform the other, Diploid are the best bet for dynamic environments.

On the performance of a hybrid genetic algorithm in dynamic environments

The ability to track the optimum of dynamic environments is important in many practical applications. In this paper, the capability of a hybrid genetic algorithm (HGA) to track the optimum in some dynamic environments is investigated for different functional dimensions, update frequencies, and displacement strengths in different types of dynamic environments. Experimental results are reported by using the HGA and some other existing evolutionary algorithms in the literature. The results show that the HGA has better capability to track the dynamic optimum than some other existing algorithms.

Adaptive Primal–Dual Genetic Algorithms in Dynamic Environments

Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.

Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

A hybrid immigrants scheme for genetic algorithms in dynamic environments

Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.

A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments

In this paper, an adaptive domination change mechanism for diploid genetic algorithms with discrete representations is presented. It is aimed at improving the performance of existing diploid genetic algorithms in changing environments. Diploidy acts as a source of diversity in the gene pool while the adaptive domination mechanism guides the phenotype towards an optimum. The combined effect of diploidy and the adaptive domination forms a balance between exploration and exploitation. The dominance characteristic of each locus in the population is adapted through feedback from the ongoing search process. A dynamic bit matching benchmark is used to perform controlled experiments. Controlled changes to implement different levels of change severities and frequencies are used. The testing phase consists of four stages. In the first stage, the benefits of the adaptive domination mechanism are shown by testing it against previously proposed diploid approaches. In the second stage, the same adaptive approach is applied to a haploid genetic algorithm to show the effect of the diploidy on the performance of the proposed approach. In the third stage, the levels of diversity introduced by diploidy on the genotype and maintained by the adaptive domination mechanism on the phenotype are explored. In the fourth stage, tests are performed to examine the robustness of the chosen approaches against different mutation rates. Currently, the dominance change mechanism can be applied to di-allelic or multiallelic discrete representations and promising results are obtained as a result of the tests performed.

Dynamic vehicle routing by means of a genetic algorithm

PurposeTo propose and to evaluate a new genetic algorithm (GA) for solving the dynamic pickup and delivery problem with time windows (DPDPTW).Design/methodology/approachFirst, a grouping genetic algorithm (GGA) for the (static) PDPTW is described. In order to solve the dynamic problem, the GGA then is embedded in a rolling horizon framework. Special updating mechanisms are provided which assure that reusable solution knowledge is preserved over the plan revisions. The approach is evaluated using a large number of test instances with varying degrees of dynamism.FindingsThe experimental results have demonstrated that the proposed approach is able to find high‐quality solutions when compared with two comparative heuristics.Research limitations/implicationsFuture research will be dedicated to the following issues: testing the proposed method using larger problem instances, using more sophisticated objective functions in order to further improve and evaluate the approach, integrating fast local search techniques into the genetic search, speeding up the algorithm by optimizing its implementation.Practical implicationsIn order to meet the increasing demands on the flexibility and the promptness of transportation services, algorithms are needed for dispatching transportation requests that arrive dynamically during the planning period. The findings of this contribution justify the employment of GAs in such dynamic transportation planning environments.Originality/valueAlthough the application of GAs in dynamic environments attracts growing attention, up to now no such algorithm has been published for the DPDPTW. To the best of the author's knowledge, this is the first time a GA has been applied to the DPDPTW.