Competitive coevolution for defense and security

2021 ◽  
Author(s):  
Sean N. Harris ◽  
Daniel R. Tauritz
Keyword(s):  
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

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

2022 ◽  
Vol 72 ◽  
pp. 103340
Author(s):  
Anisha Isaac ◽  
H. Khanna Nehemiah ◽  
Snofy D. Dunston ◽  
V.R. Elgin Christo ◽  
A. Kannan
Keyword(s):  
Feature Selection ◽  
Pulmonary Emphysema ◽  
Competitive Coevolution

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.

Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution

2010 ◽  
Vol 22 (4) ◽  
pp. 475-484 ◽  
Author(s):  
Ryosuke Chiba ◽  
◽  
Tamio Arai ◽  
Jun Ota ◽  
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Design Method ◽  
Flow Path ◽  
Automated Guided Vehicle ◽  
Simultaneous Design ◽  
Combined Flow ◽  
Competitive Coevolution ◽  
System Effectiveness ◽  
Agv Systems

An effective and robust flow path network is desired in Automated Guided Vehicle (AGV) systems. A design process to obtain the desired flow path network in AGV systems is proposed in this paper. Our proposed method can make flow path networks robust against tasks, which include pick-up point, drop-off point and throughput and number of AGVs . It is important for this robust flow path network that the kinds of tasks be of various and non-linear to the system effectiveness. The problem is solved by the design method of various kinds of tasks that are difficult for AGV systems using Genetic Algorithm (GA). An effective flow path network is designed with GA simultaneously because the difficult tasks and number of AGVs depend on the flow path networks. Competitive coevolution is applied to the simultaneous design. AGV systems can be effective with uni/bi-directional combined flow path networks which utilize just simple routings. Results of the design are shown through simulations, and the designed flow path network makes it possible to complete various tasks with various numbers of AGVs.

A competitive coevolution scheme inspired by DE

2014 ◽  
Author(s):  
Guomundur Einarsson ◽  
Thomas Runarsson ◽  
Gunnar Stefansson
Keyword(s):  
Competitive Coevolution

scholarly journals Cyclic Incrementality in Competitive Coevolution: Evolvability through Pseudo-Baldwinian Switching-Genes

2016 ◽  
Vol 22 (3) ◽  
pp. 319-352 ◽  
Author(s):  
Rick Janssen ◽  
Stefano Nolfi ◽  
Pim Haselager ◽  
Ida Sprinkhuizen-Kuyper
Keyword(s):  
Evolutionary Dynamics ◽  
Hierarchical Classification ◽  
Simulation Studies ◽  
Cluster Data ◽  
Competitive Coevolution ◽  
Fitness Measures ◽  
Red Queen Effect ◽  
The Red Queen ◽  
Bioinformatics Community

Coevolving systems are notoriously difficult to understand. This is largely due to the Red Queen effect that dictates heterospecific fitness interdependence. In simulation studies of coevolving systems, master tournaments are often used to obtain more informed fitness measures by testing evolved individuals against past and future opponents. However, such tournaments still contain certain ambiguities. We introduce the use of a phenotypic cluster analysis to examine the distribution of opponent categories throughout an evolutionary sequence. This analysis, adopted from widespread usage in the bioinformatics community, can be applied to master tournament data. This allows us to construct behavior-based category trees, obtaining a hierarchical classification of phenotypes that are suspected to interleave during cyclic evolution. We use the cluster data to establish the existence of switching-genes that control opponent specialization, suggesting the retention of dormant genetic adaptations, that is, genetic memory. Our overarching goal is to reiterate how computer simulations may have importance to the broader understanding of evolutionary dynamics in general. We emphasize a further shift from a component-driven to an interaction-driven perspective in understanding coevolving systems. As yet, it is unclear how the sudden development of switching-genes relates to the gradual emergence of genetic adaptability. Likely, context genes gradually provide the appropriate genetic environment wherein the switching-gene effect can be exploited.

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