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Improving Parallel State-Space Exploration Using Genetic Algorithms
[chapter]
2018
Lecture Notes in Computer Science
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This paper proposes a new technique that aims to tackle this limitation by generating artificial initial states, using genetic algorithms. ...
Threads are then launched from these states and thus explore different parts of the state space. Our prototype implementation runs 10% faster than state-of-the-art algorithms. ...
Algorithm 5 describes this parallel state-space exploration using genetic algorithm. ...
doi:10.1007/978-3-030-00359-3_9
fatcat:ms4i645s7bcnboveprudc2m2sq
Parallel surrogate-assisted global optimization with expensive functions – a survey
2016
Structural And Multidisciplinary Optimization
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A key issue focused on in this review is how different algorithms balance exploration and exploitation. ...
This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. ...
The exploration part of the algorithm is enhanced by the fact that EI is actually a conditioned expected improvement, conditioned on improvement actually taking place. ...
doi:10.1007/s00158-016-1432-3
fatcat:p6gewbd5o5dfpcmciznpnqm7am
Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms
2017
Open Cybernetics and Systemics Journal
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This paper presents a brief review of techniques used to allow evolutionary algorithms to adapt to optimization problems in dynamic environments, through exploration of the control parameters of genetic ...
A description of some of the most used evolutionary techniques is included, with major emphasis on genetic algorithms and their relationship with the problem of adaptation to the environment. ...
over-represented in the genotype space corresponding to the initial population Dandass [33] Genetic Algorithm Explores three different representations for the same scheduling problem in real-time parallel ...
doi:10.2174/1874110x01711010001
fatcat:7qs6kxudznaqlet4zduvi6z25y
DeSpErate: Speeding-up design space exploration by using predictive simulation scheduling
2014
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2014
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The design space exploration (DSE) phase is used to tune configurable system parameters and it generally consists of a multiobjective optimization (MOO) problem. ...
and to obtain a high speedup with respect to state of the art approaches, without compromising the accuracy of exploration results. ...
Space Exploration (DSE) approach. ...
doi:10.7873/date.2014.231
dblp:conf/date/MarianiPZS14
fatcat:im5buc3nxvfozfivtpvpoqup2y
Design and Analysis of Multi - Heuristic Based Solution for Task Graph Scheduling Problem
2019
International Journal of Engineering and Advanced Technology
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Previously these algorithms have been individually reported to be efficient in some certain restricted environment parameters with certain limitations; offering very preliminary improvement on the state ...
Genetic Algorithm based task graph scheduling solution and perform a comparative study of aforementioned algorithms. ...
Mutation Mutation operator like crossover operator, is used for the purpose of exploration of the problem space. ...
doi:10.35940/ijeat.f8680.088619
fatcat:6ayyimhotfautopalpjmfaspmu
Parallel Recombinative Reinforcement Learning: A Genetic Approach
1996
Journal of Intelligent Systems
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The method allows an efficient parallel implementation and is based on the combination of genetic algorithms and reinforcement learning schemes. ...
Each probability vector represents the adaptable parameters of a team of stochastic units whose binary outputs provide a point of the function state space. ...
One promising such technique is Parallel Recombinative Simulated Annealing (PRSA) (Mahfoud and Goldberg, 1995) to improve many of the weaknesses of both genetic algorithms and simulated annealing. ...
doi:10.1515/jisys.1996.6.2.145
fatcat:wdifpbejjjco7dgmyeiwhaae64
Balanced Explore-Exploit clustering based Distributed Evolutionary Algorithm for Multi-objective Optimisation
2011
Studies in Informatics and Control
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In this paper, we present a new clustering-based parallel multi-objective evolutionary algorithm that balances between the two main concepts in metaheuristics, which are exploration and exploitation of ...
Another promising alternative is to create new distributed schemes that improve the behaviour of the search process of such algorithms. ...
[ 6 ] 6 De Toro et al. have presented a similar parallel scheme as [18] with the difference of using a Single Front Genetic Algorithm on each processor. ...
doi:10.24846/v20i2y201102
fatcat:7bnfl5zz4nb3bmeb77x5xdsmi4
Hyperparameter Tuning for Deep Reinforcement Learning Applications
[article]
2022
arXiv
pre-print
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In this paper, we propose a distributed variable-length genetic algorithm framework to systematically tune hyperparameters for various RL applications, improving training time and robustness of the architecture ...
However, setting the right hyperparameters can have a huge impact on the deployed solution performance and reliability in the inference models, produced via RL, used for decision-making. ...
With extensions using mpipy, this processing time can be further improved with parallelism, to be extended in future. ...
arXiv:2201.11182v1
fatcat:ilhx5djtlzbcdcohcax6mj5dda
Hybrid Optimisation Method Using PGA and SQP Algorithm
2007
2007 IEEE Symposium on Foundations of Computational Intelligence
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This paper investigates the hybridisation of two very different optimisation methods, namely the Parallel Genetic Algorithm (PGA) and Sequential Quadratic Programming (SQP) Algorithm. ...
Experiments show the hybrid method effectively combines the robust and global search property of Parallel Genetic Algorithms with the high convergence velocity of the Sequential Quadratic Programming Algorithm ...
This is expected, since the hybrid search algorithm uses an identical PGA in the primary stage for exploration of the search space. ...
doi:10.1109/foci.2007.372150
dblp:conf/foci/SkinnerNL07
fatcat:z6wjpafhgvctrdveqhyskhrd4e
On Integrating Population-Based Metaheuristics with Cooperative Parallelism
2018
2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
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We discuss an initial prototype implementation for Quadratic Assignment Problem combining a Genetic Algorithm with two local search procedures. ...
Being able to run different heuristics in parallel, CPLS has opened a new way to hybridize metaheuristics, thanks to its cooperative parallelism mechanism. ...
This value is very high for classical genetic algorithms but in our contect, the genetic algorithm is mainly used to ensure a high degree of diversification for other explorers. ...
doi:10.1109/ipdpsw.2018.00100
dblp:conf/ipps/LopezMDA18
fatcat:sqyta5x2dvf47ii7hvpurc64rq
Towards hybrid evolutionary algorithms
1999
International Transactions in Operational Research
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In order to add some rational to these issues, we study the structure of search spaces and attempt to relate it to the performance of algorithms. ...
This paper briefly reviews the current knowledge we have on search spaces of combinatorial optimization problems. ...
A parallel tabu search is used to solve the QAP, while a genetic algorithm achieves a diversification task, which is formulated as an optimization problem. ...
doi:10.1016/s0969-6016(99)00019-2
fatcat:cr525zd6hvgg3gfe55642t5jsi
Towards hybrid evolutionary algorithms
1999
International Transactions in Operational Research
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In order to add some rational to these issues, we study the structure of search spaces and attempt to relate it to the performance of algorithms. ...
This paper briefly reviews the current knowledge we have on search spaces of combinatorial optimization problems. ...
A parallel tabu search is used to solve the QAP, while a genetic algorithm achieves a diversification task, which is formulated as an optimization problem. ...
doi:10.1111/j.1475-3995.1999.tb00173.x
fatcat:axqcpomtujcx5fd23rsnwqo54m
A multi-objective decision-theoretic exploration algorithm for platform-based design
2011
2011 Design, Automation & Test in Europe
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Instead of using semi-random search algorithms (like simulated annealing, tabu search, genetic algorithms, etc.), we use the domain knowledge derived from the platform architecture to set-up the exploration ...
Results show that the exploration can be performed with 10% of the simulations necessary for state-of-the-art exploration algorithms and with unrivaled accuracy (0.6 ± 0.05% error). ...
the following contributions: • A new value function definition • Improved accuracy: MOMDP sports a six-fold accuracy improvement compared to MDP and surpasses nine other state-of-the-art exploration algorithms ...
doi:10.1109/date.2011.5763311
dblp:conf/date/BeltrameN11
fatcat:kunynkdobrhn5ie3s3u6z6i3wa
Self-adaptive hybrid genetic algorithm using an ant-based algorithm
2014
2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA)
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An ant colony optimization algorithm is used to monitor the behavior of a genetic-local hybrid algorithm and dynamically adjust its control parameters to optimize the exploitation exploration balance according ...
In this paper, a novel form of hybridization between an ant-based algorithm and a genetic-local hybrid algorithm is proposed. ...
An ant colony model for continuous search spaces has been used to improve the quality of the solutions produced by a genetic algorithm [8] . ...
doi:10.1109/roma.2014.7295881
fatcat:lsymjyogpjestigimdj52f2cnu
Hybridizing exact methods and metaheuristics: A taxonomy
2009
European Journal of Operational Research
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Pareto fronts obtained by the Adaptive Genetic/Memetic Algorithm are strictly improved by this hybridization. ...
The five classes proposed are: Use exact algorithms to explore large neighborhoods in local search algorithms. ...
doi:10.1016/j.ejor.2007.07.035
fatcat:uq3aphcfqbgujf5idpcdqxd66i
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