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An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization
2006
The 2006 IEEE International Joint Conference on Neural Network Proceedings
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This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems. ...
The optimization of classification systems is often confronted by the solution over-fit problem. ...
OVER-FIT EFFECTS IN MULTI-OBJECTIVE OPTIMIZATION Solution over-fit also occurs when classification systems are optimized using evolutionary algorithms in a wrapper approach. ...
doi:10.1109/ijcnn.2006.247331
dblp:conf/ijcnn/RadtkeWS06
fatcat:smjtcqjwnjdgzfi2os5dqkd7uq
An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization
The 2006 IEEE International Joint Conference on Neural Network Proceedings
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This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems. ...
The optimization of classification systems is often confronted by the solution over-fit problem. ...
OVER-FIT EFFECTS IN MULTI-OBJECTIVE OPTIMIZATION Solution over-fit also occurs when classification systems are optimized using evolutionary algorithms in a wrapper approach. ...
doi:10.1109/ijcnn.2006.1716553
fatcat:bdddwdzsprfb3i4sli3drmkfcu
Surrogate-assisted evolutionary computation: Recent advances and future challenges
2011
Swarm and Evolutionary Computation
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Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single-or multi-objective optimization problems, but also ...
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms ...
In that work, two types of surrogates are used in the local search of an evolutionary multi-objective optimization: One for getting a reliable local prediction and the other for a higher degree of diversity ...
doi:10.1016/j.swevo.2011.05.001
fatcat:z3f6vey24fawbmz6gkpcjbnq5q
Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm
2011
Polish Maritime Research
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An objective of the present paper was to develop an evolutionary algorithm for multiobjective optimization of the structural elements of large spatial sections of ships. ...
They show that the proposed genetic algorithm may be considered an efficient tool for multi-objective optimization of ship structures. ...
On the basis of such superficial quality analysis can be proposed the statement that the combined fitness multi-objective optimization algorithm can be a more efficient strategy for multi-objective optimization ...
doi:10.2478/v10012-011-0020-0
fatcat:6v3xjldlqbethlqeaf5fpihqjm
A systems approach to evolutionary multiobjective structural optimization and beyond
2009
IEEE Computational Intelligence Magazine
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Multi-objective evolutionary algorithms (MOEAs) have shown to be effective in solving a wide range of test problems. However, it is not straightforward to apply MOEAs to complex real-world problems. ...
This paper discusses the major challenges we face in applying MOEAs to complex structural optimization, including the involvement of time-consuming and multi-disciplinary quality evaluation processes, ...
A generic framework for evolutionary multi-objective structural optimization (EMOSO) is illustrated in Fig. 1 . ...
doi:10.1109/mci.2009.933094
fatcat:3lkyzxi5p5gddclxmw64ajecg4
Beyond black-box optimization: a review of selective pressures for evolutionary robotics
2014
Evolutionary Intelligence
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Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -evolutionary algorithms -to the design of robots, or parts of robots. ...
The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. ...
When each sub-goal is an objective in a Pareto-based multi-objective evolutionary algorithm, the Pareto-optimal set is made of the best individual for each sub-task, but also of all the other Pareto-optimal ...
doi:10.1007/s12065-014-0110-x
fatcat:zev64dia3vcszgefpujd4xxgzi
Synergies between Evolutionary Algorithms and Reinforcement Learning
2015
Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference - GECCO Companion '15
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optimization • Evolutionary Multi-objective optimization (EMO) à a branch of MCDM concerned with multi-objective optimization • Co-evolutionary algorithm à assume two populations of individuals that compete ...
conditions
Evolutionary Computation (EC): an introduction
• EC is a subfield of global optimization
• popular for its large number of variants for different types of real-
world applications
• Evolutionary ...
in multi-objective search spaces [Inja et al, 2014] ...
doi:10.1145/2739482.2756582
dblp:conf/gecco/Drugan15
fatcat:5jedfs4jmfgclcpppzcjvz6yvu
Data-Driven Evolutionary Optimization: An Overview and Case Studies
2018
IEEE Transactions on Evolutionary Computation
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for fitness evaluations. ...
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. ...
Thanks also go to Dr Jan O Jansen for providing us with the data of the trauma system problem and to Dr John Doherty for his support for using CFD simulations in airfoil optimization. ...
doi:10.1109/tevc.2018.2869001
fatcat:zn6mqhj7xrdfjc3kc3wxxacsli
Many-Objective Optimization of a Hybrid Car Controller
[chapter]
2015
Lecture Notes in Computer Science
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Such an approximation of objective fitness may be, for instance, generalization performance. ...
A commonly used approach in Evolutionary Algorithms for Dynamic Constrained Optimization Problems forces re-evaluation of a population of individuals whenever the landscape changes. ...
lines of code, that enable handling multi-value fitness functions with a single-objective evolutionary optimizer. ...
doi:10.1007/978-3-319-16549-3_48
fatcat:yvlv6xnggnf3jn7vdnnwih6chy
Survey on Multi-Objective Evolutionary Algorithms
2019
Journal of Physics, Conference Series
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Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. ...
Finally several viewpoints for the future research of MOEA are presented. ...
Greenhouse control, robot motion planning, control scheme design, etc., for example, using multi-objective evolutionary algorithms to solve problems in navigation of humanoid robots [58] . ...
doi:10.1088/1742-6596/1288/1/012057
fatcat:jugqqqyonvhoflgkvs5xmdtjga
Multi-Tasking Genetic Algorithm (MTGA) for Fuzzy System Optimization
[article]
2019
arXiv
pre-print
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Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. ...
Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. ...
THE WATER-LEVEL CONTROL PLANT FOR FITNESS EVALUATION. ...
arXiv:1812.06303v2
fatcat:su2mxotgundopciymzbqnbmtpa
A Novel Hybrid Evolutionary Algorithm for Solving Multi-Objective Optimization Problems
[chapter]
2012
Lecture Notes in Computer Science
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This paper applies an evolutionary optimization scheme , inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategies , to find approximate solutions for multiobjective ...
The desired control function may be subjected to severe changes over a period of time . In response to deficiency , the process of dispersal has been modified in the MOIWO . ...
The purpose of this study is to find approximate solutions of MOCPs by using an evolutionary optimization strategy . ...
doi:10.1007/978-3-642-31588-6_17
fatcat:kaqc473rkff4xhfabvsnirpygm
Evolutionary parameter optimization of a fuzzy controller which is used to control a sewage treatment plant
2010
Water Science and Technology
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Therefore, an evolution strategy was employed which uses the multi-objective ranking as used by the SPEA2 algorithm. Optimal parameters were first evolved on simulated sewage treatment plants. ...
With this contribution, a dedicated multi-objective evolutionary algorithm is developed to optimize these parameters. ...
A (4/2,20) evolution strategy was used which was extended with an archive similar to the successful strength pareto evolutionary algorithm 2 (SPEA2) multi-objective optimization method (Zitzler et al. ...
doi:10.2166/wst.2010.778
pmid:20057091
fatcat:f4vpgoqvzbhsnmajd6nb52htpm
Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems
2009
Scientific Journal of Riga Technical University Computer Sciences
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Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems The paper presents a taxonomic analysis of existing hybrid multi-objective evolutionary algorithms ...
For that, the properties of evolutionary algorithms and the requirements made to solving the problem considered are determined. ...
Classifying the Hybrid Multi-Objective Evolutionary Algorithms Over the last years, an interest in the hybridisation of multi-objective evolutionary algorithms has risen considerably among researchers. ...
doi:10.2478/v10143-010-0001-2
fatcat:rwfjfvhzxvcphfhzpvqxkrmvli
Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning
2014
2014 14th International Conference on Quality Software
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Multi-Objective Evolutionary Algorithms (MOEAs) have shown to be suitable candidates to find these trade-offs and have been even applied for cloud architecture optimizations [21] . ...
The conciliation of these conflicting objectives has to be achieved by exhibiting trade-offs. ...
Sputnik introduces a favoritism operator approach in the mutation process described as follows: We consider a multi-objective evolutionary optimization of f with n objectives ( f 1 , f 2 ,..., f n ). ...
doi:10.1109/qsic.2014.44
dblp:conf/qsic/KatebFBT14
fatcat:bekqtozxhfdq3fzuwbkw6iekay
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