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Oriented Crossover Genetic

We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation.

Image Threshold Segmentation Based on Auxiliary Individual Oriented Crossover Genetic Algorithm

Oriented Crossover Genetic

In this paper, an auxiliary individual oriented crossover genetic algorithm is adopted to optimize the problem.

Design of Water Distribution Network via Auxiliary Individual Oriented Genetic Algorithm

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crossover genetic algorithm

We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation.

Image Threshold Segmentation Based on Auxiliary Individual Oriented Crossover Genetic Algorithm

crossover genetic algorithm

This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems.

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

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crossover genetic operator

Regarding the genetic algorithm, we propose an initial population to boost the convergence of the optimization process, whilst the adopted mutation and crossover genetic operators result in feasible individuals.

A surrogate similarity measure for the mean-variance frontier optimisation problem under bound and cardinality constraints

crossover genetic operator

The first binary variant is based on Sigmoid transfer function and indicated as sigABC, while the second and the third binary variants are based on exclusive OR (xor) and crossover genetic operators, respectively, and are indicated as xorABC and crossoverABC.

Binary Artificial Bee Colony Algorithms for {0-1} Advertisement Problem

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10.1109/ICII.2019.00076

We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation.

Image Threshold Segmentation Based on Auxiliary Individual Oriented Crossover Genetic Algorithm

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10.1007/S10732-018-9385-X

This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems.

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

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10.1007/S00500-019-03790-1

We have constructed a new index model named data vector (DV) tree using crossover genetic algorithm which is a component of the soft computing.

Attribute-based hierarchical file encryption for efficient retrieval of files by DV index tree from cloud using crossover genetic algorithm

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10.1109/ISITIA.2019.8937184

The simulation result shows that the modified crossover genetic algorithm is able to generate optimal solutions based on the desired criterion.

Path Planning for Differential Drive Mobile Robot to Avoid Static Obstacles Collision using Modified Crossover Genetic Algorithm

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10.1016/J.APENERGY.2019.113456

A new optimization algorithm, the self-crossover genetic algorithm, is proposed.

A micro-market module design for university demand-side management using self-crossover genetic algorithms

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10.1109/ICWAPR48189.2019.8946477

A new optimization algorithm called the Self-Crossover Genetic Algorithm is proposed to support model optimization.

Optimization Of Production Scheduling Using Self-Crossover Genetic Algorithm

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10.1080/01605682.2019.1657367

Regarding the genetic algorithm, we propose an initial population to boost the convergence of the optimization process, whilst the adopted mutation and crossover genetic operators result in feasible individuals.

A surrogate similarity measure for the mean-variance frontier optimisation problem under bound and cardinality constraints

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10.1109/ICEEE2019.2019.00025

The first binary variant is based on Sigmoid transfer function and indicated as sigABC, while the second and the third binary variants are based on exclusive OR (xor) and crossover genetic operators, respectively, and are indicated as xorABC and crossoverABC.

Binary Artificial Bee Colony Algorithms for {0-1} Advertisement Problem

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10.1109/ICII.2019.00075

In this paper, an auxiliary individual oriented crossover genetic algorithm is adopted to optimize the problem.

Design of Water Distribution Network via Auxiliary Individual Oriented Genetic Algorithm

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10.1088/1755-1315/227/2/022018

The improved chaotic crossover genetic algorithm is used to solve the planning model.

Flexibility planning of transmission grid based on correlation of generation and transmission

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10.1016/j.asoc.2018.11.012

Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures.

Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification

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Crossover Genetic
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What is/are Crossover Genetic?

Crossover Genetic - We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation. [1] This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. [2] We have constructed a new index model named data vector (DV) tree using crossover genetic algorithm which is a component of the soft computing. [3] The simulation result shows that the modified crossover genetic algorithm is able to generate optimal solutions based on the desired criterion. [4] A new optimization algorithm, the self-crossover genetic algorithm, is proposed. [5] A new optimization algorithm called the Self-Crossover Genetic Algorithm is proposed to support model optimization. [6] Regarding the genetic algorithm, we propose an initial population to boost the convergence of the optimization process, whilst the adopted mutation and crossover genetic operators result in feasible individuals. [7] The first binary variant is based on Sigmoid transfer function and indicated as sigABC, while the second and the third binary variants are based on exclusive OR (xor) and crossover genetic operators, respectively, and are indicated as xorABC and crossoverABC. [8] In this paper, an auxiliary individual oriented crossover genetic algorithm is adopted to optimize the problem. [9] The improved chaotic crossover genetic algorithm is used to solve the planning model. [10] Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures. [11]

Oriented Crossover Genetic

We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation. [1] In this paper, an auxiliary individual oriented crossover genetic algorithm is adopted to optimize the problem. [2]

crossover genetic algorithm

We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation. [1] This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. [2] We have constructed a new index model named data vector (DV) tree using crossover genetic algorithm which is a component of the soft computing. [3] The simulation result shows that the modified crossover genetic algorithm is able to generate optimal solutions based on the desired criterion. [4] A new optimization algorithm, the self-crossover genetic algorithm, is proposed. [5] A new optimization algorithm called the Self-Crossover Genetic Algorithm is proposed to support model optimization. [6] In this paper, an auxiliary individual oriented crossover genetic algorithm is adopted to optimize the problem. [7] The improved chaotic crossover genetic algorithm is used to solve the planning model. [8] Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures. [9]

crossover genetic operator

Regarding the genetic algorithm, we propose an initial population to boost the convergence of the optimization process, whilst the adopted mutation and crossover genetic operators result in feasible individuals. [1] The first binary variant is based on Sigmoid transfer function and indicated as sigABC, while the second and the third binary variants are based on exclusive OR (xor) and crossover genetic operators, respectively, and are indicated as xorABC and crossoverABC. [2]