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» Parallelism and evolutionary algorithms
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116
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ACTA
2005
87views more  ACTA 2005»
15 years 3 months ago
Hybrid networks of evolutionary processors are computationally complete
A hybrid network of evolutionary processors (an HNEP) consists of several language processors which are located in the nodes of a virtual graph and able to perform only one type o...
Erzsébet Csuhaj-Varjú, Carlos Mart&i...
120
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GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
15 years 9 months ago
Discovering event evidence amid massive, dynamic datasets
Automated event extraction remains a very difficult challenge requiring information analysts to manually identify key events of interest within massive, dynamic data. Many techniq...
Robert M. Patton, Thomas E. Potok
106
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CIG
2005
IEEE
15 years 9 months ago
Co-evolutionary Strategies for an Alternating-Offer Bargaining Problem
Abstract- In this paper, we apply an Evolutionary Algorithm (EA) to solve the Rubinstein’s Basic AlternatingOffer Bargaining Problem, and compare our experimental results with it...
Nanlin Jin, Edward P. K. Tsang
151
Voted
GECCO
2005
Springer
125views Optimization» more  GECCO 2005»
15 years 9 months ago
Improving EA-based design space exploration by utilizing symbolic feasibility tests
This paper will propose a novel approach in combining Evolutionary Algorithms with symbolic techniques in order to improve the convergence of the algorithm in the presence of larg...
Thomas Schlichter, Christian Haubelt, Jürgen ...
142
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CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 3 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná