What Is Autonomous Search?

Hamadi, Youssef; Monfroy, Éric; Saubion, Frédéric

doi:10.1007/978-1-4419-1644-0_11

Cited by 31 publications

(30 citation statements)

References 47 publications

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“…It can be seen that, even though GE-HH does not obtain the best solutions for all instances, over all, it obtains competitive results especially when considering the percentage deviation (∆(%)) from the best known value found in the literature. If we consider an individual comparison, GE-HH is able to obtain better solutions on instances 8, 12, 11, 6, 7 and 2 compared to 7 andMc 11 reported result for Rye-s-93 instance (we suspect, due to the complexity and inconsistencies in these instances).…”

Section: A Problems Domain I: Computational Results On Exam Timetablmentioning

confidence: 76%

“…they are able to obtain high quality results for just a few problem instances, but usually fail on other instances even of the same problem and cannot be directly applied to other optimization problems. Of course, the No Free Lunch Theorem [5] states that a general search method does not exist, but it does not mean that we cannot investigate more general search algorithms to explore the limits of such an algorithm [6][7][8].…”

Section: Which Algorithm Is the Most Suitable For The Problem At Handmentioning

confidence: 99%

“…It is well established that the efficiency of any problem solver relies on its ability to explore regions of the search space, which is strongly influenced by its structures and parameter values [7], [10], [12]. Therefore, the performance of any search methodology can potentially be enhanced by automatically adjusting its structures and/or parameter values.…”

Section: A the Proposed Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems

Sabar

Ayob

Kendall

et al. 2013

IEEE Trans. Evol. Computat.

109

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Section: A Problems Domain I: Computational Results On Exam Timetablmentioning

confidence: 76%

Section: Which Algorithm Is the Most Suitable For The Problem At Handmentioning

confidence: 99%

Section: A the Proposed Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems

Sabar

Ayob

Kendall

et al. 2013

IEEE Trans. Evol. Computat.

109

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“…Also, we want to investigate how is the performance of autonomous search tuning algorithms [74] in big data environment. Finally, we also want to explore the performance of other metaheuristics in Big data Frameworks.…”

Section: Discussionmentioning

confidence: 99%

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

et al. 2018

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The progress of metaheuristic techniques, big data, and the Internet of things generates opportunities to performance improvements in complex industrial systems. This article explores the application of Big Data techniques in the implementation of metaheuristic algorithms with the purpose of applying it to decision-making in industrial processes. This exploration intends to evaluate the quality of the results and convergence times of the algorithm under different conditions in the number of solutions and the processing capacity. Under what conditions can we obtain acceptable results in an adequate number of iterations? In this article, we propose a cuckoo search binary algorithm using the MapReduce programming paradigm implemented in the Apache Spark tool. The algorithm is applied to different instances of the crew scheduling problem. The experiments show that the conditions for obtaining suitable results and iterations are specific to each problem and are not always satisfactory.

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“…Interesting summaries of statistical machine learning methods applied for large-scale optimization are presented in [1]. Autonomous search methods that adapt the search performance to the problem at hand are described in [15], which defines a metric to classify problem solvers based on their computation characteristics. The authors first identify a set of rules describing search processes, and then they classify existing search paradigms from different but related areas, such as the Satisfiability and the Constraint satisfaction problem.…”

Section: Reinforcement Learning For Optimizationmentioning

confidence: 99%

An Investigation of Reinforcement Learning for Reactive Search Optimization

Battiti

Campigotto

2011

Autonomous Search

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Reactive Search Optimization advocates the adoption of learning mechanisms as an integral part of a heuristic optimization scheme. This work studies reinforcement learning methods for the online tuning of parameters in stochastic local search algorithms. In particular, the reactive tuning is obtained by learning a (near-)optimal policy in a Markov decision process where the states summarize relevant information about the recent history of the search. The learning process is performed by the Least Squares Policy Iteration (LSPI) method. The proposed framework is applied for tuning the prohibition value in the Reactive Tabu Search, the noise parameter in the Adaptive Walksat, and the smoothing probability in the Reactive Scaling and Probabilistic Smoothing (RSAPS) algorithm. The novel approach is experimentally compared with the original ad hoc reactive schemes.

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What Is Autonomous Search?

Cited by 31 publications

References 47 publications

Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems

Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

An Investigation of Reinforcement Learning for Reactive Search Optimization

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