Decomposition of Fitness Functions in Random Heuristic Search

The goal of minimal attribute reduction is to find the minimal subsetRof the condition attribute setCsuch thatRhas the same classification quality asC. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.

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Copy-move forgery detection using SURF and a local heuristic search scheme

Future Computer and Information Technology ◽

10.2495/icfcit131011 ◽

2013 ◽

Author(s):

Mei-Yi Wu

Keyword(s):

Heuristic Search ◽

Forgery Detection ◽

Copy Move Forgery Detection

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Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1651-07 ◽

1998 ◽

Vol 1651 (1) ◽

pp. 48-52 ◽

Cited By ~ 1

Author(s):

Jeffrey L. Adler

Keyword(s):

Heuristic Search ◽

Transportation Networks ◽

Transportation Network ◽

Search Problems ◽

A Algorithm ◽

Running Time ◽

Search Effort ◽

Wide Range ◽

Path Search ◽

Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

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Faculty Opinions recommendation of The impact of environmental fluctuations on evolutionary fitness functions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725857970.793536170 ◽

2017 ◽

Author(s):

Thierry Mora

Keyword(s):

Fitness Functions ◽

Environmental Fluctuations ◽

The Impact ◽

Evolutionary Fitness

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Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999200424075719 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ge Weiqing ◽

Cui Yanru

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Task Scheduling ◽

Scheduling Algorithm ◽

Task Completion ◽

Improved Genetic Algorithm ◽

Fitness Functions ◽

Task Scheduling Algorithm ◽

Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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