Evaluation of Crossover and Partial Lane Closure Strategies for Interstate Work Zones in Indiana
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There are two main lane closure strategies for Interstate highway work zones, crossover and partial lane closure. Depending on the situation one or the other strategy may be more desirable. However, little research has been done to develop a systematic method for selecting appropriate lane closure strategies. The selection of appropriate lane closure strategies at Interstate highway work zones is discussed.Keywords:
Closure (psychology)
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Crossover study
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This paper introduces the optimization methods of Genetic Algorithm.Based on the Different Location Crossover and the Same Location Crossover,a new leading crossover is proposed.Then it is a self-adaptive manner judgment to choose which crossover is used before the crossover operator.At last,five different tests of the simulation function are given.The results show that the leading crossover is more efficient to improve convergence than other crossovers.And the new method is easy to find the optimal solution.
Operator (biology)
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This paper proposes a modified Order crossover operator for genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP) effectively. As the main time consuming process of crossover operator and schemata preserving process in crossover is the hole filling done in Order Crossover operator, so if the swath size is not to be too long and also depending on the problem size then it might be proven to find near optimum solutions in effective and efficient manner. The Modified Order Crossover operator constructs an offspring from a pair of parents using the existing Order Crossover operator with the enhancement on swath length (the size of a chromosome which is between two crossover sites). The efficiency of the Modified Order Crossover is compared as against some of the existing crossover operators; namely, Partially Mapped Crossover (PMX), Order Crossover (OX) & Cyclic Crossover (CX) for some benchmark TSPLIB instances. Experimental results suggest that the new crossover operator enabled improved results compared to the PMX, OX and CX for the five Travelling salesman problems tested
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It is known that selection and crossover operators contribute to generating solutions in genetic programming (GP). Traditionally, crossover points are selected randomly by a normal (canonical) crossover. However, the traditional method has several difficulties, in that building blocks (i.e. effective partial programs) are broken because of blind application of the normal crossover. This paper proposes a depth-dependent crossover for GP, in which the depth selection ratio is varied according to the depth of a node. This proposed method accumulates building blocks via the encapsulation of the depth-dependent crossover. We compare the performance of GP with depth-dependent crossover with that with normal crossover. Our experimental results clarify that the superiority of the proposed crossover to the normal method.
Crossover study
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This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) have been conducted to evaluate the proposed methods, which are compared to the well-known Modified crossover operator and partially mapped Crossover (PMX) crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.
Operator (biology)
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Crossover operators play an important role in evolutionary strategy. Several different crossover operators have been developed in the past decades. However, each crossover operator is only efficient in some type of problems, but fails in another one. In order to overcome the disadvantage, a possible solution is to use a mixed crossover strategy, which mixes various crossover operators. In this paper, an example of such strategies is introduced which employs four different crossover strategies: two-point crossover, uniform crossover, arithmetic crossover. The simulation results show that the mixed crossover strategy is superior to any pure crossover strategy.
Operator (biology)
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This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some travelling salesman problems have been conducted to evaluate the proposed methods, which are compared to the well-known modified crossover operator and partially mapped crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.
Operator (biology)
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This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some travelling salesman problems have been conducted to evaluate the proposed methods, which are compared to the well-known modified crossover operator and partially mapped crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.
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Crossover operator is one of the important genetic operators,which is the processing of getting two different new individuals by operating parental individuals.It has important influence on the searching results of genetic algorithm.Crossover operator can deliver good genes to the next generation.Improved crossover operator proposed in this paper,which is improved from crossover probability and strategy,can be applied to function optimization.Comparing to the typical genetic algorithm,it has more optimum performance,and can get better solutions.
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Genetic operator
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