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Component-wise Analysis of Automatically Designed Multiobjective Algorithms on Constrained Problems [article]

Yuri Lavinas and Marcelo Ladeira and Gabriela Ochoa and Claus Aranha
2022 arXiv   pre-print
Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better; while the update strategy was more influential  ...  We apply this methodology to a well-performing Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems.  ...  RELATED WORK Most approaches on the automatic design of evolutionary algorithms focus on creating templates that can instantiate many algorithms and their parameter settings for performance improvements  ... 
arXiv:2203.13447v6 fatcat:6fzfb45fvzestitdxfzfweybg4

On Self-Adaptive Mutation Restarts for Evolutionary Robotics with Real Rotorcraft [article]

Gerard David Howard
2017 arXiv   pre-print
This paper focuses on the level at which evolutionary rate restarts are applied in population-based algorithms with more than 1 evolutionary operator.  ...  Rate restarts are typically employed to remedy this, but thus far have only been applied in Evolutionary Robotics for mutation-only algorithms.  ...  Future research will consider the e ect of problem di culty on the restart strategy performance.  ... 
arXiv:1703.10754v2 fatcat:j7dynow6nbglfbtezac2mzpcme

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm [chapter]

Jean-Marc Montanier, Nicolas Bredeche
2011 Studies in Computational Intelligence  
A new implementation of the algorithm, termed (1+1)-restart-online algorithm, is described and implemented within the Symbrion robotic Cortex M3 microcontroller as well as on a real mobile robot.  ...  The work presented here extends the (1+1)online algorithm, which was introduced in [3]. This algorithm is a variation of a famous Evolution Strategies [18] adapted to autonomous robots.  ...  In this scope, Evolutionary Robotics provides optimization algorithms based on Evolutionary Algorithms which are fitted to this class of problems.  ... 
doi:10.1007/978-3-642-18272-3_11 fatcat:qxme7ruwurbildnqm3cbusjj5i

A Reflective PN-Based Approach to Dynamic Workflow Change

Lorenzo Capra, Walter Cazzola
2007 Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007)  
We propose and discuss the adoption of a recent Petri Net based reflective model (based on classical PN) as a support to dynamic workflow design, by addressing a localized problem: how to determine what  ...  The design of dynamic workflows needs adequate modeling/specification formalisms and tools to soundly handle possible changes occurring during workflow operation.  ...  permits some kind of on-the-fly verification of workflow changes during evolutionary strategy execution.  ... 
doi:10.1109/synasc.2007.64 dblp:conf/synasc/CapraC07 fatcat:x42cc6u6wzbhziqmjiaxkjgxoa

Approaching the bi-objective critical node detection problem with a smart initialization-based evolutionary algorithm

Eliézer Béczi, Noémi Gaskó
2021 PeerJ Computer Science  
Evolutionary multi-objective algorithms (EMOA) are a straightforward choice to solve this type of problem.  ...  We propose three different smart initialization strategies which can be incorporated into any EMOA. These initialization strategies take into account the basic properties of the networks.  ...  of restart is 0.2; and for the Deg algorithm, x = k 3 .  ... 
doi:10.7717/peerj-cs.750 pmid:34805505 pmcid:PMC8576564 fatcat:rox2piuduja5nenju5kdn2aury

Evolutionary programming with ensemble of explicit memories for dynamic optimization

E. L. Yu, P. N. Suganthan
2009 2009 IEEE Congress on Evolutionary Computation  
The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results  ...  The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving  ...  We test the algorithm on the benchmarks of CEC 2009 Competition on Dynamic Optimization.  ... 
doi:10.1109/cec.2009.4982978 dblp:conf/cec/YuS09 fatcat:kia6qsvzuje6xpkjqvzdvaf6je

Hyper-learning for population-based incremental learning in dynamic environments

Shengxiang Yang, Hendrik Richter
2009 2009 IEEE Congress on Evolutionary Computation  
Recently, the PBIL algorithm has been applied for dynamic optimization problems.  ...  The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning.  ...  The effect of the learning rate and the hyper-learning scheme for PBIL algorithms in dynamic environments were experimentally studied based on a series of dynamic test problems.  ... 
doi:10.1109/cec.2009.4983011 dblp:conf/cec/YangR09 fatcat:6cd5yojuh5do5irokyudrfppxq

An analytical study of GPU computation for solving QAPs by parallel evolutionary computation with independent run

Shigeyoshi Tsutsui, Noriyuki Fujimoto
2010 IEEE Congress on Evolutionary Computation  
This paper proposes an evolutionary algorithm for solving QAPs with parallel independent run using GPU computation and gives a statistical analysis on how speedup can be attained with this model.  ...  With the proposed model, we achieve a GPU computation performance that is nearly proportional to the number of equipped multi-processors (MPs) in the GPUs.  ...  CONCLUSIONS In this paper, we proposed an evolutionary algorithm for solving QAPs with parallel independent runs using GPU computation and gave an analysis of the results.  ... 
doi:10.1109/cec.2010.5585960 dblp:conf/cec/TsutsuiF10 fatcat:oc3n3blibvcpjggyg3yg6hugam

A Study on Evolutionary Algorithms and Its Applications

2022 Electrical and Automation Engineering  
Evolutionary methods are based on the concepts of biological evolution.  ...  Genetic algorithms (GAs), evolutionary strategies (ESs), differential evolution (DE) and distribution algorithm evaluation (EDAs) are used.  ...  In particular, see the programming algorithms for taxonomy-rule discovery for genetic analysis, the review of learning taxonomy systems (a type of algorithm based on a combination of EA and reinforcement  ... 
doi:10.46632/eae/1/1/1 fatcat:7so6ywbsz5hmxeezpt2tr7eqfe

Iterated local search with Powell's method: a memetic algorithm for continuous global optimization

Oliver Kramer
2010 Memetic Computing  
The approach is analyzed experimentally on a set of well known test problems and compared to a stateof-the-art technique, i.e., a restart variant of the Covariance Matrix Adaptation Evolution Strategy  ...  Further experiments on the perturbation mechanism, population sizes, and problems with noise complete the analysis of the hybrid methodology and lead to parameter recommendations.  ...  [34] introduce the concept of local optimum structure for the analysis of Lamarckian memetic algorithms. They generalize the notion of neighborhood to connectivity structure.  ... 
doi:10.1007/s12293-010-0032-9 fatcat:knm7aaspurenllug2iso7rdb3i

Inoculation to initialise evolutionary search [chapter]

Patrick D. Surry, Nicholas J. Radcliffe
1996 Lecture Notes in Computer Science  
Such ideas also have implications for algorithmic restarts after convergence.  ...  Non-random initialisation, or inoculation, of the population in an evolutionary algorithm provides a way to incorporate such knowledge.  ...  Timothy Harding for their assistance and encouragements in carrying out the reported experiments on credit scoring and oil-field production scheduling respectively.  ... 
doi:10.1007/bfb0032789 fatcat:7xmf4ct2j5hn7gg74zih5tnj4i

Cyber-EDA: Estimation of Distribution Algorithms with Adaptive Memory Programming

Peng-Yeng Yin, Hsi-Li Wu
2013 Mathematical Problems in Engineering  
The experimental result on benchmark TSP instances supports our anticipation that the AMP strategies can enhance the performance of classic EDA by deriving a better approximation for the true distribution  ...  By iterative filtering for quality solution from competing ones, the probability model eventually approximates the distribution of global optimum solutions.  ...  Acknowledgment This research is partially supported by National Science Council of ROC, under Grant NSC 98-2410-H-260-018-MY3.  ... 
doi:10.1155/2013/132697 fatcat:dsa4ru7qa5aq7fxawd6vu5lozy

Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization

Xingguang Peng, Yapei Wu
2018 Complexity  
In the experimental study, the proposed CC-SMP is compared to 7 state-of-the-art CC algorithms on 20 benchmark functions with 1000 dimensionality.  ...  A restart-after-stagnation procedure is incorporated to help the child populations adapt to the dynamic landscape.  ...  Acknowledgments This work has been supported by the National Natural Science Foundation of China (nos. 6117235 and 61473233).  ... 
doi:10.1155/2018/9267054 fatcat:pjlcz4bzvnas7cgf6mlmxtzspm

An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

Hossam M. J. Mustafa, Masri Ayob, Mohd Zakree Ahmad Nazri, Graham Kendall, Yang Li
2019 PLoS ONE  
The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process.  ...  of the optimisation algorithm.  ...  These algorithms have the same evolutionary phases of AMADA except restart phase.  ... 
doi:10.1371/journal.pone.0216906 pmid:31137034 pmcid:PMC6538400 fatcat:g7k2eduwrzhsnjk6dcn3zp35fi

Runtime Analysis of Random Local Search on JUMP function with Reinforcement Based Selection of Auxiliary Objectives

Denis Antipov, Arina Buzdalova
2017 2017 IEEE Congress on Evolutionary Computation (CEC)  
One of them is helpful during the first phase of optimization and another one is helpful during the last phase. On other stages they are constant, so they neither help nor slow optimization down.  ...  However, it has not been theoretically analysed whether this method can efficiently optimize nonmonotonic functions using simple evolutionary algorithms and reinforcement learning agents.  ...  To sum up, the future work is to expand the algorithm on some more practical problems by finding the way of selecting a restart strategy.  ... 
doi:10.1109/cec.2017.7969567 dblp:conf/cec/AntipovB17 fatcat:viqlhqa74vcsjim27h7ph4r3uy
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