Evolving Opposition-Based Pareto Solutions: Multiobjective Optimization Using Competitive Coevolution

2008 ◽  
pp. 161-206 ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo
Keyword(s):  
Multiobjective Optimization ◽  
Pareto Solutions ◽  
Competitive Coevolution

HYBRIDIZATION OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHM WITH COEVOLUTION FOR ENEMY TEAM IN MS. PAC-MAN GAME

2014 ◽  
Vol 2 (1) ◽  
pp. 36-51
Author(s):  
Tse guan Tan ◽  
Jason Teo ◽  
On Chin Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Technique ◽  
Dynamic Game ◽  
Evolution Strategy ◽  
Evolutionary Multiobjective Optimization ◽  
Competitive Coevolution ◽  
Virtual Creatures

AbstrakKini, semakin ramai penyelidik telah menunjukkan minat mengkaji permainan Kecerdasan Buatan (KB).Permainan seumpama ini menyediakan tapak uji yang sangat berguna dan baik untuk mengkaji asasdan teknik-teknik KB. Teknik KB, seperti pembelajaran, pencarian dan perencanaan digunakan untukmenghasilkan agen maya yang mampu berfikir dan bertindak sewajarnya dalam persekitaran permainanyang kompleks dan dinamik. Dalam kajian ini, satu set pengawal permainan autonomi untuk pasukan hantudalam permainan Ms. Pac-man yang dicipta dengan menggunakan penghibridan Evolusi PengoptimumanMultiobjektif (EPM) dan ko-evolusi persaingan untuk menyelesaikan masalah pengoptimuman dua objektifiaitu meminimumkan mata dalam permainan dan bilangan neuron tersembunyi di dalam rangkaianneural buatan secara serentak. Arkib Pareto Evolusi Strategi (APES) digunakan, teknik pengoptimumanmultiobjektif ini telah dibuktikan secara saintifik antara yang efektif di dalam pelbagai aplikasi. Secarakeseluruhannya, keputusan eksperimen menunjukkan bahawa teknik pengoptimuman multiobjektif bolehmendapat manfaat daripada aplikasi ko-evolusi persaingan Abstract Recently, researchers have shown an increased interest in game Artificial Intelligence (AI). Gamesprovide a very useful and excellent testbed for fundamental AI research. The AI techniques, such aslearning, searching and planning are applied to generate the virtual creatures that are able to think andact appropriately in the complex and dynamic game environments. In this study, a set of autonomousgame controllers for the ghost team in the Ms. Pac-man game are created by using the hybridizationof Evolutionary Multiobjective Optimization (EMO) and competitive coevolution to solve the bi-objectiveoptimization problem of minimizing the game's score by eating Ms. Pac-man agent and the number ofhidden neurons in neural network simultaneously. The Pareto Archived Evolution Strategy (PAES) is usedthat has been proved to be an effective and efficient multiobjective optimization technique in variousapplications. Overall, the results show that multiobjective optimizer can benefit from the application ofcompetitive coevolutionary

Classification as Clustering: A Pareto Cooperative-Competitive GP Approach

2011 ◽  
Vol 19 (1) ◽  
pp. 137-166 ◽  
Author(s):  
Andrew R. McIntyre ◽  
Malcolm I. Heywood
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Natural Environment ◽  
Team Member ◽  
Population Based ◽  
Classification Performance ◽  
Model Complexity ◽  
Good Balance ◽  
Competitive Coevolution ◽  
Do So

Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.

An Evolutionary Clustering Approach to Pareto Solutions in Multiobjective Optimization

2002 ◽  
Author(s):  
Min Joong Jeong ◽  
Sinobu Yoshimura
Keyword(s):  
Multiobjective Optimization ◽  
Engineering Design ◽  
Function Space ◽  
Clustering Algorithm ◽  
Computational Effort ◽  
Local Optimum ◽  
Pareto Solutions ◽  
Evolutionary Clustering ◽  
Interpretation Process ◽  
Clustering Approach

Pareto solutions in multiobjective optimization are very problematic to measuring the characteristics of solutions for engineering design because of their considerable variety in function space and parameter space. To overcome these situations, a clustering-based interpretation process for Pareto solutions is considered. For better competitive clustering algorithm, we propose an evolutionary clustering algorithm — ECA. The ECA requires less computational effort, and overcomes local optimum of the K-means clustering algorithm and its related algorithms. Effectiveness of the method is examined in detail through the comparison with other algorithms.

Sufficient conditions for global weak Pareto solutions in multiobjective optimization

Positivity ◽  
2012 ◽  
Vol 16 (3) ◽  
pp. 579-602 ◽  
Author(s):  
Truong Q. Bao ◽  
Boris S. Mordukhovich
Keyword(s):  
Multiobjective Optimization ◽  
Sufficient Conditions ◽  
Pareto Solutions

scholarly journals Sensitivity of Pareto Solutions in Multiobjective Optimization

2005 ◽  
Vol 126 (2) ◽  
pp. 247-264 ◽  
Author(s):  
A. Balbás ◽  
E. Galperin ◽  
P. Jiménez. Guerra
Keyword(s):  
Multiobjective Optimization ◽  
Pareto Solutions

RELIABILITY AND REDUNDANCY APPORTIONMENT OPTIMIZATION USING INTERACTIVE PHYSICAL PROGRAMMING

2004 ◽  
Vol 11 (03) ◽  
pp. 213-222 ◽  
Author(s):  
HONG-ZHONG HUANG ◽  
ZHI-GANG TIAN ◽  
YING-KUI GU
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Pareto Solutions ◽  
Physical Programming ◽  
Good Convergence ◽  
Convergence Performance

In this paper, a new multiobjective optimization approach named interactive physical programming is proposed and used to solve the reliability and redundancy apportionment optimization problem. Interactive physical programming extends physical programming6 to an interactive framework. After the designer specifies which objectives need to be improved and which objectives can be sacrificed, interactive physical programming can obtain the Pareto solutions satisfying such improving preferences. It has good convergence performance, and can obtain satisfactory design in the end. Interactive physical programming has been successfully applied to a reliability and redundancy apportionment optimization problem. It provides a new effective approach for reliability optimization.

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