GA-Optimal fitness functions

1998 ◽  
pp. 95-102 ◽  
Author(s):  
Joseph L. Breeden
Keyword(s):  
Fitness Functions

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

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.

Beyond Interactive Evolution: Expressing Intentions Through Fitness Functions

Leonardo ◽  
2016 ◽  
Vol 49 (3) ◽  
pp. 251-256 ◽  
Author(s):  
Penousal Machado ◽  
Tiago Martins ◽  
Hugo Amaro ◽  
Pedro H. Abreu
Keyword(s):  
Fitness Function ◽  
Interactive Art ◽  
Support Tool ◽  
Generative Art ◽  
Fitness Functions ◽  
Meta Level ◽  
Evolutionary Art ◽  
Creativity Support ◽  
The Creation ◽  
The Impact

Photogrowth is a creativity support tool for the creation of nonphotorealistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.

Fitness Functions for Alternative Developmental Pathways in the Timing of Diapause Induction

1988 ◽  
Vol 131 (5) ◽  
pp. 678-699 ◽  
Author(s):  
Fritz Taylor ◽  
John B. Spalding
Keyword(s):  
Diapause Induction ◽  
Developmental Pathways ◽  
Fitness Functions

Dynamic Correcting Dispersion Parameters of Lagrangian Puff Model in Atmospheric Tracer Experiments

2014 ◽  
Author(s):  
Yuanwei Ma ◽  
Dezhong Wang ◽  
Zhilong Ji ◽  
Nan Qian
Keyword(s):  
Numerical Simulation ◽  
Dispersion Model ◽  
Fitness Function ◽  
Atmospheric Dispersion ◽  
Correction Procedure ◽  
Model Errors ◽  
Dispersion Parameters ◽  
Fitness Functions ◽  
Atmospheric Dispersion Models ◽  
Atmospheric Tracer

In atmospheric dispersion models of nuclear accident, the empirical dispersion coefficients were obtained under certain experiment conditions, which is different from actual conditions. This deviation brought in the great model errors. A better estimation of the radioactive nuclide’s distribution could be done by correcting coefficients with real-time observed value. This reverse problem is nonlinear and sensitive to initial value. Genetic Algorithm (GA) is an appropriate method for this correction procedure. Fitness function is a particular type of objective function to achieving the set goals. To analysis the fitness functions’ influence on the correction procedure and the dispersion model’s forecast ability, four fitness functions were designed and tested by a numerical simulation. In the numerical simulation, GA, coupled with Lagrange dispersion model, try to estimate the coefficients with model errors taken into consideration. Result shows that the fitness functions, in which station is weighted by observed value and by distance far from release point, perform better when it exists significant model error. After performing the correcting procedure on the Kincaid experiment data, a significant boost was seen in the dispersion model’s forecast ability.

scholarly journals Independence Conservation and Evolutionary Algorithms

2020 ◽  
Vol 2 (1) ◽  
pp. 32-35
Author(s):  
Eric Holloway
Keyword(s):  
Information Theory ◽  
Evolutionary Algorithms ◽  
Algorithmic Information Theory ◽  
Fitness Functions ◽  
Information Law ◽  
Algorithmic Information

Leonid Levin developed the first stochastic conservation of information law, describing it as "torturing an uninformed witness cannot give information about the crime."  Levin's law unifies both the deterministic and stochastic cases of conservation of information.  A proof of Levin's law from Algorithmic Information Theory is given as well as a discussion of its implications in evolutionary algorithms and fitness functions.

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