Digital Image Evolution of Artwork Without Human Evaluation Using the Example of the Evolving Mona Lisa Problem

Whether for optimizing the speed of microprocessors or for sequence analysis in molecular biology — evolutionary algorithms are used in astoundingly many fields. Also, the art was influenced by evolutionary algorithms — with principles of natural evolution works of art that can be created or imitated, whereby initially generated art is put through an iterated process of selection and modification. This paper covers an application in which given images are emulated evolutionary using a finite number of semi-transparent overlapping polygons, which also became known under the name “Evolution of Mona Lisa”. In this context, different approaches to solve the problem are tested and presented here. In particular, we want to investigate whether Hill Climbing Algorithm in combination with Delaunay Triangulation and Canny Edge Detector that extracts the initial population directly from the original image performs better than the conventional Hill Climbing and Genetic Algorithm, where the initial population is generated randomly.

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Empirical Evaluation of Hill Climbing Algorithm

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2017100102 ◽

2017 ◽

Vol 8 (4) ◽

pp. 27-40 ◽

Cited By ~ 2

Author(s):

Manju Khari ◽

Prabhat Kumar

Keyword(s):

Collective Behavior ◽

Empirical Evaluation ◽

Optimal Solution ◽

Research Community ◽

Hill Climbing ◽

Test Cases ◽

Average Percentage ◽

Hill Climbing Algorithm ◽

Steepest Ascent ◽

Better Than

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

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Multiple-Reservoir Scheduling Using β-Hill Climbing Algorithm

Journal of Intelligent Systems ◽

10.1515/jisys-2017-0159 ◽

2019 ◽

Vol 28 (4) ◽

pp. 559-570 ◽

Cited By ~ 7

Author(s):

Emad Alsukni ◽

Omar Suleiman Arabeyyat ◽

Mohammed A. Awadallah ◽

Laaly Alsamarraie ◽

Iyad Abu-Doush ◽

...

Keyword(s):

Evolutionary Algorithms ◽

Local Search ◽

Comparative Evaluation ◽

Optimization Problem ◽

Hill Climbing ◽

High Dimensionality ◽

Hill Climbing Algorithm ◽

Reservoir Systems ◽

Multiple Reservoir ◽

Water Amount

Abstract The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm.

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