Real-Coded Memetic Algorithms with Crossover Hill-Climbing

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive 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 to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

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A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem

Evolutionary Computation ◽

10.1162/evco_a_00026 ◽

2011 ◽

Vol 19 (3) ◽

pp. 345-371 ◽

Cited By ~ 7

Author(s):

Daniel Karapetyan ◽

Gregory Gutin

Keyword(s):

Local Search ◽

Population Size ◽

Assignment Problem ◽

Memetic Algorithm ◽

Optimization Problems ◽

Memetic Algorithms ◽

Multidimensional Assignment Problem ◽

Local Searches ◽

Wide Range ◽

Local Search Procedure

Memetic algorithms are known to be a powerful technique in solving hard optimization problems. To design a memetic algorithm, one needs to make a host of decisions. Selecting the population size is one of the most important among them. Most of the algorithms in the literature fix the population size to a certain constant value. This reduces the algorithm's quality since the optimal population size varies for different instances, local search procedures, and runtimes. In this paper we propose an adjustable population size. It is calculated as a function of the runtime of the whole algorithm and the average runtime of the local search for the given instance. Note that in many applications the runtime of a heuristic should be limited and, therefore, we use this bound as a parameter of the algorithm. The average runtime of the local search procedure is measured during the algorithm's run. Some coefficients which are independent of the instance and the local search are to be tuned at the design time; we provide a procedure to find these coefficients. The proposed approach was used to develop a memetic algorithm for the multidimensional assignment problem (MAP). We show that our adjustable population size makes the algorithm flexible to perform efficiently for a wide range of running times and local searches and this does not require any additional tuning of the algorithm.

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One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mathematics ◽

10.3390/math9233030 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3030

Author(s):

Raúl Mencía ◽

Carlos Mencía

Keyword(s):

Local Search ◽

Electric Vehicles ◽

Memetic Algorithm ◽

Memetic Algorithms ◽

Hill Climbing ◽

Search Methods ◽

Local Search Methods ◽

Local Search Procedure ◽

One Machine Scheduling ◽

One Machine

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

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Memetic Algorithms for Continuous Optimisation Based on Local Search Chains

Evolutionary Computation ◽

10.1162/evco.2010.18.1.18102 ◽

2010 ◽

Vol 18 (1) ◽

pp. 27-63 ◽

Cited By ~ 110

Author(s):

Daniel Molina ◽

Manuel Lozano ◽

Carlos García-Martínez ◽

Francisco Herrera

Keyword(s):

Local Search ◽

Memetic Algorithm ◽

Memetic Algorithms ◽

Internal Variables ◽

Search Methods ◽

Local Tuning ◽

Local Search Methods ◽

Optimisation Methods ◽

Parameter Values ◽

Local Search Operator

Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods, it is interesting to build prospective memetic algorithm models with them. This paper presents the concept of local search chain as a springboard to design memetic algorithm approaches that can effectively use intense continuous local search methods as local search operators. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration (initial solution, strategy parameter values, internal variables, etc.) reached by this one. The proposed memetic algorithm favours the formation of local search chains during the memetic algorithm run with the aim of concentrating local tuning in search regions showing promise. In order to study the performance of the new memetic algorithm model, an instance is implemented with CMA-ES as an intense local search method. The benefits of the proposal in comparison to other kinds of memetic algorithms and evolutionary algorithms proposed in the literature to deal with continuous optimisation problems are experimentally shown. Concretely, the empirical study reveals a clear superiority when tackling high-dimensional problems.

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Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

Evolutionary Computation ◽

10.1162/106365600568103 ◽

2000 ◽

Vol 8 (1) ◽

pp. 61-91 ◽

Cited By ~ 93

Author(s):

Peter Merz ◽

Bernd Freisleben

Keyword(s):

Local Search ◽

Fitness Landscape ◽

Memetic Algorithm ◽

Search Space ◽

Memetic Algorithms ◽

Dependency Graph ◽

Gene Interactions ◽

Local Minima ◽

Problem Size ◽

Better Than

The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced. In particular, the local structure and the regularity of the dependency graph seems to be important for the performance of an algorithm, and in fact, algorithms that exploit these properties perform significantly better than others which do not. It will be shown that a simple hybrid multi-start local search exploiting locality in the structure of the graphs is able to find optimum or near optimum solutions very quickly. However, if the problem size increases or the graphs become unstructured, a memetic algorithm (a genetic algorithm incorporating local search) is shown to be much more effective.

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A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.300-301.645 ◽

2013 ◽

Vol 300-301 ◽

pp. 645-648 ◽

Cited By ~ 1

Author(s):

Yung Chien Lin

Keyword(s):

Memetic Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Search Space ◽

Memetic Algorithms ◽

Batch Processes ◽

Mixed Integer ◽

Integer Optimization ◽

Genetic Operators ◽

Search Methods

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

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Self-Adaptation of Mutation Operator and Probability for Permutation Representations in Genetic Algorithms

Evolutionary Computation ◽

10.1162/evco_a_00006 ◽

2010 ◽

Vol 18 (3) ◽

pp. 491-514 ◽

Cited By ~ 39

Author(s):

Martin Serpell ◽

James E. Smith

Keyword(s):

Mutation Rate ◽

Poor Performance ◽

Adaptive Methods ◽

Optimal Choice ◽

Problem Instance ◽

Mutation Operator ◽

Genetic Operators ◽

Mutation Operators ◽

Wide Range ◽

Self Adaptation

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one “optimal choice”; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via self-adaptation, thus removing this nontrivial task from the GA user and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with “static” operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms.

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Solving the tool switching problem with memetic algorithms

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006041100014x ◽

2011 ◽

Vol 26 (2) ◽

pp. 221-235 ◽

Cited By ~ 11

Author(s):

Jhon Edgar Amaya ◽

Carlos Cotta ◽

Antonio J. Fernández-Leiva

Keyword(s):

Flexible Manufacturing ◽

Manufacturing Systems ◽

Ad Hoc ◽

Memetic Algorithm ◽

Memetic Algorithms ◽

Hill Climbing ◽

Beam Search ◽

Steepest Ascent ◽

Tool Switching ◽

Better Than

AbstractThe tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

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A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

Mathematical Problems in Engineering ◽

10.1155/2014/867645 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Yu Lei ◽

Maoguo Gong ◽

Licheng Jiao ◽

Wei Li ◽

Yi Zuo ◽

...

Keyword(s):

Simulated Annealing ◽

Memetic Algorithm ◽

Population Diversity ◽

Genetic Operators ◽

Examination Timetabling ◽

Timetabling Problem ◽

Uncapacitated Examination Timetabling Problem

A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

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Memetic Algorithms and Their Applications in Computer Science

Advances in Computational Intelligence and Robotics - Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms ◽

10.4018/978-1-5225-2857-9.ch004 ◽

2018 ◽

pp. 73-93

Author(s):

B. K. Tripathy ◽

Sooraj T. R. ◽

R. K. Mohanty

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Local Search ◽

Computer Science ◽

Graph Coloring ◽

Data Clustering ◽

Memetic Algorithm ◽

Optimization Problems ◽

Memetic Algorithms ◽

Population Based

The term “memetic algorithm” was introduced by Moscato is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. Memetic algorithms are intrinsically concerned with exploiting all available knowledge about the problem under study. MAs are population-based metaheuristics. In this chapter we explore the applications of memetic algorithms to problems within the domains of image processing, data clustering and Graph coloring, i.e., how we can use the memetic algorithms in graph coloring problems, how it can be used in clustering based problems and how it is useful in image processing. Here, we discuss how these algorithms can be used for optimization problems. We conclude by reinforcing the importance of research on the areas of metaheuristics for optimization.

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