scholarly journals A Mathematical Design of Genetic Operators onGLn(ℤ2)

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yourim Yoon ◽  
Yong-Hyuk Kim
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Linear Algebra ◽  
Binary Representation ◽  
Two Dimensional ◽  
Genetic Operators ◽  
Binary Matrices ◽  
Mathematical Design

We study the space that consists of all nonsingular binary matrices, that is,GLn(ℤ2). The space is quite important in that it is used for the change of basis in binary representation, which is the encoding typically adopted in genetic algorithms. We analyze the properties ofGLn(ℤ2)and theoretically design possible encodings and their corresponding recombination operators for evolutionary algorithms. We present approaches based on elementary matrices of linear algebra as well as typical two-dimensional ones.

Specific Evolutionary Algorithms

1996 ◽  
Author(s):  
Thomas Bäck
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Selection Process ◽  
Evolutionary Programming ◽  
Main Stream ◽  
Genetic Operators ◽  
Optimization Task ◽  
Main Components ◽  
High Level

In this chapter, an outline of an Evolutionary Algorithm is formulated that is sufficiently general to cover at least the three different main stream algorithms mentioned before, namely, Evolution Strategies, Genetic Algorithms, and Evolutionary Programming. As in the previous chapter, algorithms are formulated in a language obtained by mixing pseudocode and mathematical notations, thus allowing for a high-level description which concentrates on the main components. These are: A population of individuals which is manipulated by genetic operators — especially mutation and recombination, but others may also be incorporated — and undergoes a fitness-based selection process, where fitness of an individual depends on its quality with respect to the optimization task.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

Assembly Planning: A Genetic Approach

1998 ◽  
Author(s):  
Shiang-Fong Chen
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
Assembly Planning ◽  
Genetic Approach ◽  
Genetic Operators ◽  
Complex Problems ◽  
All Solutions ◽  
Assembly Problem ◽  
Optimal Assembly

Abstract The difficulty of an assembly problem is the inherent complexity of possible solutions. If the most suitable plan is selected after all solutions are found, it will be very time consuming and unrealistic. Motivated by the success of genetic algorithms (GAs) in solving combinatorial and complex problems by examining a small number of possible candidate solutions, GAs are employed to find a near-optimal assembly plan for a general environment. Five genetic operators are used: tree crossover, tree mutation, cut-and-paste, break-and-joint, and reproduction. The fitness function can adapt to different criteria easily. This assembly planner can help an inexperienced technician to find a good solution efficiently. The algorithm has been fully implemented. One example product is given to show the applications and results.

scholarly journals A GENETIC BASED TECHNIQUE FOR THE DETERMINATION OF POWER SYSTEM TOPOLOGICAL OBSERVABILITY

2014 ◽  
pp. 16-21
Author(s):  
S. Vazquez-Rodriguez ◽  
R. J. Duro
Keyword(s):  
Genetic Algorithms ◽  
Power Systems ◽  
Power System ◽  
Spanning Tree ◽  
Point Of View ◽  
Genetic Operators ◽  
Building Process ◽  
Phenotype Transformation

In this paper we have addressed the problem of observability of power systems from the point of view of topological observability and using genetic algorithms for its determination. The objective is to find a way to determine if a system is observable by establishing if a spanning tree of the system that verifies certain properties with regards to the use of available measurements can be obtained. To this end we have developed a genotype-phenotype transformation scheme for genetic algorithms that permits using very simple genetic operators over integer based chromosomes which after a building process can become very complex trees. The procedure was successfully applied to standard benchmark systems and we present some results for one of them.

Approach for Negotiation Problems in Multi-Agent Systems for DC Micro Grid Using Multi-Objective Evolutionary Algorithms

2015 ◽  
Vol 713-715 ◽  
pp. 2106-2109
Author(s):  
Mauricio Mauledoux ◽  
Edilberto Mejía-Ruda ◽  
Oscar I. Caldas
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimal ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Objective ◽  
Multi Agent ◽  
Micro Grid ◽  
Optimal Space ◽  
Multi Agents

The work is devoted to solve allocation task problem in multi agents systems using multi-objective genetic algorithms and comparing the technique with methods used in game theories. The paper shows the main advantages of genetic algorithms and the way to apply a parallel approach dividing the population in sub-populations saving time in the search and expanding the coverage of the solution in the Pareto optimal space.

Multiobjective Evolutionary Algorithms MOEA to Solve Task Allocation Problems in Multiagent Systems for DC MicroGrid

2014 ◽  
Vol 700 ◽  
pp. 24-27 ◽  
Author(s):  
Mauricio Mauledoux ◽  
Edilberto Mejía-Ruda ◽  
Oscar I. Caldas
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Multiagent Systems ◽  
Task Allocation ◽  
The Other ◽  
Optimal Distribution ◽  
Multiobjective Evolutionary Algorithms ◽  
Distributed Energy ◽  
Dc Microgrid ◽  
Multi Agents

The work is devoted to solve allocation task problem in the distributed energy way in multi agents systems with multi-objective genetic algorithms. The paper shows the main advantages of genetic algorithms and the way to apply a new genetic operator using the solution information of the other agents for saving energy in the search of expand the solution of the optimal distribution.

How Genetic Algorithms Handle Pareto-Optimality in Design and Manufacturing

2007 ◽  
pp. 465-482
Author(s):  
N. Chakraborti
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Pareto Optimality ◽  
Multi Objective Optimization ◽  
Recent Past ◽  
Multi Objective ◽  
Manufacturing Sectors ◽  
Design And Manufacturing ◽  
Basic Concepts

An informal analysis is provided for the basic concepts associated with multi-objective optimization and the notion of Pareto-optimality, particularly in the context of genetic algorithms. A number of evolutionary algorithms developed for this purpose are also briefly introduced, and finally, a number of paradigm examples are presented from the materials and manufacturing sectors, where multi-objective genetic algorithms have been successfully utilized in the recent past.

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