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Automatically Improving the Anytime Behaviour of Optimisation Algorithms: Supplementary material. http:
, 2012
"... Abstract Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the ..."
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Abstract Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker's preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System, and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters.
Automatic Design of Evolutionary Algorithms for Multi-Objective Combinatorial Optimization
, 2014
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From Grammars to Parameters: Automatic Iterated Greedy Design for the Permutation Flow-shop Problem with Weighted Tardiness
, 2013
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Deconstructing multi-objective evolutionary algorithms: An iterative analysis on the permutation flow-shop problem
, 2013
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A Non-Adaptive Stochastic Local Search Algorithm for the CHeSC 2011 Competition
"... Abstract. In this work, we present our submission for the Cross-domain Heuristic Search Challenge 2011. We implemented a stochastic local search algorithm that consists of several algorithm schemata that have been offline-tuned on four sample problem domains. The schemata are based on all families o ..."
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Abstract. In this work, we present our submission for the Cross-domain Heuristic Search Challenge 2011. We implemented a stochastic local search algorithm that consists of several algorithm schemata that have been offline-tuned on four sample problem domains. The schemata are based on all families of low-level heuristics available in the framework used in the competition with the exception of crossover heuristics. Our algorithm goes through an initial phase that filters dominated low-level heuristics, followed by an algorithm schemata selection implemented in a race. The winning schemata is run for the remaining computation time. Our algorithm ranked seventh in the competition results. In this paper, we present the results obtained after a more careful tuning, and a different combination of algorithm schemata included in the final algorithm design. This improved version would rank fourth in the competition. 1
Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search
, 2012
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Grammar-based Generation of Stochastic Local Search Heuristics Through Automatic Algorithm Configuration Tools
, 2013
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication.
Anytime local search for multi-objective . . .
, 2014
"... Our world is becoming increasingly complex and many systems are key to the well-functioning of our modern society. They can be any sort of technological systems, processes, logistical organization in the industry, distribution of goods, planning and scheduling of trains and planes, design of a city ..."
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Our world is becoming increasingly complex and many systems are key to the well-functioning of our modern society. They can be any sort of technological systems, processes, logistical organization in the industry, distribution of goods, planning and scheduling of trains and planes, design of a city roadmap, control of traffic signals, and so on. In all these situations, the decisions taken have a strong impact on efficiency, be it a matter of time, resources, or any other element to be optimized. Once the problems have been fully identified and the objective has been clearly established, the actual task of finding a good (or the optimal) solution is typically very hard for humans. In fact, even assisted by computers an entire class of problems remains very hard to solve to optimality (problems that in theoretical Computer Science have been identified asNP-
