Memetic Algorithms and Their Applications in Computer Science

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.

Memetic Algorithms and Their Applications in Computer Science

2018 ◽  
pp. 1461-1482 ◽  
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.

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

2011 ◽  
Vol 19 (3) ◽  
pp. 345-371 ◽  
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.

Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

2000 ◽  
Vol 8 (2) ◽  
pp. 149-172 ◽  
Author(s):  
Joshua D. Knowles ◽  
David W. Corne
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Population Based ◽  
Evolution Strategy ◽  
Basic Algorithm ◽  
Pareto Optimal Set ◽  
Candidate Solution ◽  
Multiobjective Optimization Problems

We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1+1) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (1+1)-PAES is intended to be a baseline approach against which more involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1+λ) and (μ+λ) variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows standard statistical analysis to be carried out for comparative purposes. Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks.

scholarly journals A FRAMEWORK FOR MEMETIC ALGORITHMS

2009 ◽  
Vol 12 (11) ◽  
pp. 27-38
Author(s):  
Tuan Anh Phan ◽  
Anh Tuan Duong
Keyword(s):  
Genetic Algorithm ◽  
Design Patterns ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Object Oriented ◽  
Memetic Algorithms ◽  
Set Covering ◽  
Covering Problems ◽  
Set Covering Problems ◽  
Combinatorial Optimization Problems

Memetic algorithm, a combination of genetic algorithm with local search, is one of the most successful metaheuristics to solve complex combinatorial optimization problems. In this paper, we will introduce an object-oriented framework which allows the construction of memetic algorithms with a maximum reuse. This framework has been developed in Java using design patterns to allow its easy extension and utilization in different problem domains. Our framework has been experimented through the development of a memetic algorithm for solving set covering problems.

scholarly journals Penjadwalan Tenaga Kebidanan Menggunakan Algoritma Memetika

2018 ◽  
Vol 8 (1) ◽  
pp. 99
Author(s):  
A. Y. Erwin Dodu ◽  
Deny Wiria Nugraha ◽  
Subkhan Dinda Putra
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Public Hospital ◽  
Memetic Algorithm ◽  
Optimal Schedule ◽  
Scheduling System ◽  
Local Search Methods ◽  
Evolution Algorithm ◽  
Central Sulawesi

The problem of midwife scheduling is one of the most frequent problems in hospitals. Midwife should be available 24 hours a day for a full week to meet the needs of the patient. Therefore, good or bad midwife scheduling result will have an impact on the quality of care on the patient and the health of the midwife on duty. The midwife scheduling process requires a lot of time, effort and good cooperation between some parties to solve this problem that is often faced by the Regional Public Hospital Undata Palu Central Sulawesi Province. This research aimed to apply Memetics algorithm to make scheduling system of midwifery staff at Regional Public Hospital Undata Palu Central Sulawesi Province that can facilitate the process of midwifery scheduling as well as to produce optimal schedule. The scheduling system created will follow the rules and policies applicable in the hospital and will also pay attention to the midwife's preferences on how to schedule them according to their habits and needs. Memetics algorithm is an optimization algorithm that combines Evolution Algorithm  and Local Search method. Evolution Algorithm in Memetics Algorithm generally refers to Genetic Algorithm so that the characteristics of Memetics Algotihm are identical with  Genetic Algorithm characteristics with the addition of Local Search methods. Local Search in Memetic Algorithm aims to improve the quality of an individual so it is expected to accelerate the time to get a solution.

Analyzing self-★ island-based memetic algorithms in heterogeneous unstable environments

2016 ◽  
Vol 32 (5) ◽  
pp. 676-692 ◽  
Author(s):  
Rafael Nogueras ◽  
Carlos Cotta
Keyword(s):  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Population Based ◽  
Self Healing ◽  
Computational Power ◽  
Ability To Work ◽  
Computing Power ◽  
Simulated Environment ◽  
Computational Resources ◽  
Better Than

Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that is, different distributions of computational power among computing nodes), and we study different strategies for distributing the search among nodes. We observe that the addition of self-scaling and self-healing properties makes the memetic algorithm very robust to both system instability and computational heterogeneity. Additionally, a strategy based on distributing single islands on each computational node is shown to perform globally better than placing many such islands on each of them (either proportionally to their computing power or subject to an intermediate compromise).

Hookes-Jeeves-Based Variant of Memetic Algorithm

2016 ◽  
pp. 450-475
Author(s):  
Dipti Singh ◽  
Kusum Deep
Keyword(s):  
Genetic Algorithms ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Memetic Algorithms ◽  
Global Optimum ◽  
Benchmark Problems ◽  
New Variant ◽  
Optimum Solution ◽  
Global Optimum Solution

Due to their wide applicability and easy implementation, Genetic algorithms (GAs) are preferred to solve many optimization problems over other techniques. When a local search (LS) has been included in Genetic algorithms, it is known as Memetic algorithms. In this chapter, a new variant of single-meme Memetic Algorithm is proposed to improve the efficiency of GA. Though GAs are efficient at finding the global optimum solution of nonlinear optimization problems but usually converge slow and sometimes arrive at premature convergence. On the other hand, LS algorithms are fast but are poor global searchers. To exploit the good qualities of both techniques, they are combined in a way that maximum benefits of both the approaches are reaped. It lets the population of individuals evolve using GA and then applies LS to get the optimal solution. To validate our claims, it is tested on five benchmark problems of dimension 10, 30 and 50 and a comparison between GA and MA has been made.

scholarly journals A Multiobjective Genetic Algorithm Based on a Discrete Selection Procedure

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Qiang Long ◽  
Changzhi Wu ◽  
Xiangyu Wang ◽  
Lin Jiang ◽  
Jueyou Li
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Selection Procedure ◽  
Population Based ◽  
Pareto Solution ◽  
Multiobjective Genetic Algorithm ◽  
Trade Offs ◽  
Multiobjective Optimization Problems ◽  
Optimum Order

Multiobjective genetic algorithm (MOGA) is a direct search method for multiobjective optimization problems. It is based on the process of the genetic algorithm; the population-based property of the genetic algorithm is well applied in MOGAs. Comparing with the traditional multiobjective algorithm whose aim is to find a single Pareto solution, the MOGA intends to identify numbers of Pareto solutions. During the process of solving multiobjective optimization problems using genetic algorithm, one needs to consider the elitism and diversity of solutions. But, normally, there are some trade-offs between the elitism and diversity. For some multiobjective problems, elitism and diversity are conflicting with each other. Therefore, solutions obtained by applying MOGAs have to be balanced with respect to elitism and diversity. In this paper, we propose metrics to numerically measure the elitism and diversity of solutions, and the optimum order method is applied to identify these solutions with better elitism and diversity metrics. We test the proposed method by some well-known benchmarks and compare its numerical performance with other MOGAs; the result shows that the proposed method is efficient and robust.

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

2000 ◽  
Vol 8 (1) ◽  
pp. 61-91 ◽  
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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