On the physical interpretation of the random-search process

We consider parameter identification of a hydride decomposition model by scanning the parameter space in parallel. Such problem is resource demanding, but suits best for Desktop Grid computing. Considering task retrieval as a game, we show that the search process can be improved to produce solutions faster in comparison with random search, with no or minor additional cost.

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Genetic Algorithms as Global Random Search Methods: An Alternative Perspective

Evolutionary Computation ◽

10.1162/evco.1995.3.1.39 ◽

1995 ◽

Vol 3 (1) ◽

pp. 39-80 ◽

Cited By ~ 29

Author(s):

Charles C. Peck ◽

Atam P. Dhawan

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Random Search ◽

Schema Theory ◽

Global Optimum ◽

Global Search ◽

Search Process ◽

Sampling Distributions ◽

Alternative Perspective ◽

Global Random Search

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

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Evaluating Recommender Systems

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2021040102 ◽

2021 ◽

Vol 17 (2) ◽

pp. 25-45

Author(s):

Alaa Alslaity ◽

Thomas Tran

Keyword(s):

Recommender Systems ◽

Quantitative Study ◽

Random Search ◽

Evaluation Methods ◽

Search Process ◽

Manual Search ◽

Theoretical Side

Replicating the results of the recommender system's evaluation is one of the main concerns in the area. This paper discusses this issue from different angles: 1) It investigates the uniformity of recommenders' evaluation designs presented in practice and their consistency with the theoretical side. 2) It highlights some of the issues and challenges that face recommenders' evaluators. 3) It provides stepwise guidelines for offline evaluation settings. A quantitative study of articles published in the last decade is studied. The search process is a manual search for a conference and a random search of journals. The results show a lack of uniformity and consistency in presenting the evaluation methods. Most of the articles miss at least one evaluation aspect (i.e., some aspects are not presented in the article). These discrepancies and the wide variety of evaluation settings lead to non-replicable experiments. To mitigate this issue, the paper proposes the recommender evaluation guidelines (REval), which presents a roadmap for recommender systems' evaluators.

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Evaluation of Neural Architecture Search Approaches for Offshore Platform Offset Prediction

10.5753/eniac.2021.18264 ◽

2021 ◽

Author(s):

Tomaz M. Suller ◽

Eric O. Gomes ◽

Henrique B. Oliveira ◽

Lucas P. Cotrim ◽

Amir M. Sa’ad ◽

...

Keyword(s):

Simulated Annealing ◽

Comparative Study ◽

Random Search ◽

Center Of Gravity ◽

Offshore Platform ◽

Bayesian Optimization ◽

Optimization Process ◽

Search Process ◽

Multi Layer Perceptron ◽

Neural Architecture

This paper proposes a solution based on Multi-Layer Perceptron (MLP) to predict the offset of the center of gravity of an offshore platform. It also performs a comparative study with three optimization algorithms – Random Search, Simulated Annealing, and Bayesian Optimization (BO) – to find the best MLP architecture. Although BO obtained the best architecture in the shortest time, ablation studies developed in this paper with hyperparameters of the optimization process showed that the result is sensitive to them and deserves attention in the Neural Architecture Search process.

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