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Automated configuration of genetic algorithms by tuning for anytime performance

Furong Ye, Carola Doerr, Hao Wang, Thomas Bäck
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
CCS CONCEPTS • Theory of computation → Random search heuristics; Design and analysis of algorithms; Bio-inspired optimization.  ...  This paper summarizes our work "Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance", to appear in IEEE Transactions on Evolutionary Computation.  ...  The first family of algorithm configuration techniques were standard search heuristics such as mixed-integer evolution strategies, but more specific AC tools have been developed in recent years, among  ... 
doi:10.1145/3520304.3534075 fatcat:sszgvhxaf5gnpg4nnvaim3mfd4

Hybridization of Decomposition and Local Search for Multiobjective Optimization

Liangjun Ke, Qingfu Zhang, Roberto Battiti
2014 IEEE Transactions on Cybernetics  
It is a population based iterative method and thus an anytime algorithm.  ...  MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can  ...  Compared Algorithms 1) 2PPLS [12] : This algorithm is the best-so-far heuristic for the biobjective TSP instances used in our experimental studies. It consists of two phases.  ... 
doi:10.1109/tcyb.2013.2295886 pmid:25222724 fatcat:qrykaifvl5fa7kkhbgftaw7p3y

Anytime Pareto local search

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, Thomas Stützle
2015 European Journal of Operational Research  
Therefore, we are certain that the proposed anytime variants of PLS further improve the state-of-the-art local search algorithm for the bTSP [34] , not only in terms of anytime behavior but also on final  ...  Pareto Local Search (PLS) is a simple and effective local search method for tackling multi-objective combinatorial optimization problems.  ...  Pareto Local Search (PLS) [41] is a heuristic algorithm for tackling NP-hard MCOPs in the Pareto sense.  ... 
doi:10.1016/j.ejor.2014.10.062 fatcat:det5fayuhfb7lee3qyqcbiye7q

Pareto Local Search Algorithms for Anytime Bi-objective Optimization [chapter]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, Thomas Stützle
2012 Lecture Notes in Computer Science  
Experimental results on the bi-objective traveling salesman problem show a large improvement of the proposed anytime PLS algorithm over the classical one.  ...  Pareto local search (PLS) is an extension of iterative improvement methods for multi-objective combinatorial optimization problems and an important part of several state-of-the-art multi-objective optimizers  ...  Here, we generate a set of 5 high-quality solutions by running the anytime two-phase local search (TPLS) algorithm [5] .  ... 
doi:10.1007/978-3-642-29124-1_18 fatcat:6hub43pscfbohme6o2u5ekohau

Adaptive "Anytime" Two-Phase Local Search [chapter]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, Thomas Stützle
2010 Lecture Notes in Computer Science  
Two-Phase Local Search (TPLS) is a general algorithmic framework for multi-objective optimization.  ...  In particular, we propose two weight setting strategies that show better anytime search characteristics than the original weight setting strategy used in the TPLS algorithm.  ...  The available approaches for tackling these problems with stochastic local search (SLS) algorithms can roughly be classified as following two main search paradigms [1] : algorithms that follow a component-wise  ... 
doi:10.1007/978-3-642-13800-3_5 fatcat:zs4ytow3nbha3h65w4k5x5un4i

MOEA/D-ACO: A Multiobjective Evolutionary Algorithm Using Decomposition and AntColony

Liangjun Ke, Qingfu Zhang, Roberto Battiti
2013 IEEE Transactions on Cybernetics  
An ant group maintains a pheromone matrix, and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem.  ...  We also demonstrate that the heuristic information matrices in MOEA/D-ACO are crucial to the good performance of MOEA/D-ACO for the knapsack problem.  ...  Fig. 2 plots the distribution of the final approximation with the lowest IGD value among 30 runs of each algorithm for each biobjective test instance.  ... 
doi:10.1109/tsmcb.2012.2231860 pmid:23757576 fatcat:h3naekzwdbcaliclxakpb5g3by

A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

Lan Xu, Yiliu Tu, Yuting Zhang
2020 Mathematical Problems in Engineering  
Particularly, an improved grasshopper optimization-based bitarget optimization algorithm (GROBO) is proposed to solve the biobjective programming model for service matching in CL.  ...  A framework for the algorithm-based CL platform is established, based on which, the operational mode of it is described in detail.  ...  Xianlei Lu for the helpful comments and suggestions to improve the quality of the paper. is work was supported by the Humanities and Social Science Foundation of Ministry of Education under Grant no. 19YJA880068  ... 
doi:10.1155/2020/3298460 fatcat:3lzqmgcx7ba27br743on7jb75q

Subset Approximation of Pareto Regions with Bi-objective A*

Nicolás Rivera, Jorge A. Baier, Carlos Hernández
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To address this issue, we present a new approach to subset approximation of the solution set, that can be used as the basis for an anytime bi-objective search algorithm.  ...  In addition, even though bi-objective search algorithms generate the Pareto set incrementally, they do so exhaustively.  ...  heuristic search algorithms.  ... 
doi:10.1609/aaai.v36i9.21276 fatcat:wbq5q6mxx5bqzolpjpb2y73roa

A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, Thomas Stützle
2011 Computers & Operations Research  
The proposed algorithm combines two search methods, two-phase local search and Pareto local search, which are representative of two different, but complementary, paradigms for multi-objective optimisation  ...  The design of the hybrid algorithm is based on a careful experimental analysis of crucial algorithmic components of these two search methods.  ...  Therefore, large instances are often tackled by means of approximate (heuristic) algorithms, and among these, stochastic local search (SLS) algorithms [8] have proven to be particularly effective.  ... 
doi:10.1016/j.cor.2010.10.008 fatcat:vfyu77fvvfdynhcqcaxavnwdau

Harmony search algorithms for inventory management problems

Ata Allah Taleizadeh
2012 African Journal of Business Management  
In recent years, harmony search (HS) algorithms have gained significant attentions for their abilities to solve difficult problems in engineering.  ...  The computational performance of the HS method on solving these four optimization problems will be compared with other meta-heuristic algorithms such as genetic algorithm (GA), simulated annealing (SA)  ...  To solve the models, four kinds of meta-heuristic algorithms or meta-heuristic hybrid algorithms: harmony search, particle swarm optimization, genetic algorithms, simulated annealing, fuzzy simulation,  ... 
doi:10.5897/ajbm12.154 fatcat:sauoxkhk3jbcpnwtof33hjtqmq

Representative Solutions for Bi-Objective Optimisation

Emir Demirovi?, Nicolas Schwind
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We implement our algorithm and empirically illustrate the efficiency on two families of benchmarks.  ...  This raises two issues: 1) time complexity, as the Pareto front in general can be infinite for continuous problems and exponentially large for discrete problems, and 2) lack of decisiveness.  ...  This is used in an anytime depth-first search algorithm that eventually enumerates the whole Pareto front.  ... 
doi:10.1609/aaai.v34i02.5501 fatcat:wrfgo6n3bzhxbpaym5hqdluyni

Heuristic Rectangle Splitting: Leveraging Single-Objective Heuristics to Efficiently Solve Multi-Objective Problems [article]

Piotr Matl, Richard F. Hartl, Thibaut Vidal
2017 arXiv   pre-print
We propose an improved version of the classical ECM adapted to the challenges and requirements specific to heuristic search.  ...  Despite its theoretical properties and conceptual simplicity, the epsilon-constraint method has been largely ignored in the domain of heuristics and remains associated mostly with exact algorithms.  ...  Summary We propose to extend the exact -constraint-based Box Algorithm of Hamacher et al. (2007) for use with heuristics.  ... 
arXiv:1705.10174v1 fatcat:ba7scgv2z5fqlohmudj7mfxngm

Heuristic and Meta-Heuristic Optimization Models for Task Scheduling in Cloud-Fog Systems: A Review

Mohammed Abdulredha, Bara'a Attea, Adnan Jabir
2020 Iraqi Journal for Electrical And Electronic Engineering  
In this article, a summary of heuristic and meta-heuristic methods for solving the task scheduling optimization in cloud-fog systems is presented.  ...  Thus, both heuristic and meta-heuristic techniques have been utilized to provide optimal or near-optimal solutions within an acceptable time frame for such problems.  ...  Thus, search and optimization algorithms are typically generated using some heuristics to solve certain problems.  ... 
doi:10.37917/ijeee.16.2.11 fatcat:5stmfoh7anc7bi6fre5r6m65va

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search [chapter]

Jérémie Dubois-Lacoste, Manuel López-Ibáñez, Thomas Stützle
2013 Studies in Computational Intelligence  
The hybridization of these two strategies provides a general framework for engineering stochastic local search algorithms that can be used to improve over the state-of-the-art for several, widely studied  ...  Most of these metaheuristics follow one of the two main paradigms to tackle such problems in a heuristic way. The first paradigm is to rely on Pareto dominance when exploring the search space.  ...  Hamacher and Ruhe [34] combined the two search paradigms to tackle the biobjective minimum spanning tree problem.  ... 
doi:10.1007/978-3-642-30671-6_3 fatcat:b3mpxkb2vrfflkwmxjn3jxvc4y

A New Approach to Iterative Deepening Multiobjective A* [chapter]

J. Coego, L. Mandow, J. L. Pérez de la Cruz
2009 Lecture Notes in Computer Science  
This paper presents IPID, a new exact algorithm based on iterative deepening, that finds the set of all Paretooptimal paths for a search problem in a graph with vectorial costs.  ...  Many real world search problems involve different objectives, usually in conflict. In these cases the cost of a transition is given by a cost vector.  ...  Both best-first and depth-first algorithms have been designed for this task; however, in tree-shaped search spaces depth-first algorithms are the natural choice, since -contrary to bestfirst algorithms-they  ... 
doi:10.1007/978-3-642-10291-2_27 fatcat:cxfwke5aszhc3occdjleh5qc7e
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