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GECCO
2006
Springer
189views Optimization» more  GECCO 2006»
9 years 10 months ago
Game theory as a new paradigm for phenotype characterization of genetic algorithms
In this paper, it is presented a new way to characterize the phenotype in the context of Genetic Algorithms through the use of Game Theory as a theoretical foundation to define a ...
Otávio Noura Teixeira, Artur Noura Teixeira...
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
9 years 10 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
GECCO
2006
Springer
164views Optimization» more  GECCO 2006»
9 years 10 months ago
Adaptation for parallel memetic algorithm based on population entropy
In this paper, we propose the island model parallel memetic algorithm with diversity-based dynamic adaptive strategy (PMADLS) for controlling the local search frequency and demons...
Jing Tang, Meng-Hiot Lim, Yew-Soon Ong
GECCO
2006
Springer
172views Optimization» more  GECCO 2006»
9 years 10 months ago
Evolving boolean networks to find intervention points in dengue pathogenesis
We use probabilistic boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are mode...
Philip Tan, Joc Cing Tay
GECCO
2006
Springer
144views Optimization» more  GECCO 2006»
9 years 10 months ago
When lisp is faster than C
This paper compares the performance of the program evaluation phase of genetic programming using C and Common Lisp. A simple experiment is conducted, and the conclusion is that ge...
Børge Svingen
GECCO
2006
Springer
134views Optimization» more  GECCO 2006»
9 years 10 months ago
PSO and multi-funnel landscapes: how cooperation might limit exploration
Particle Swarm Optimization (PSO) is a population-based optimization method in which search points employ a cooperative strategy to move toward one another. In this paper we show ...
Andrew M. Sutton, Darrell Whitley, Monte Lunacek, ...
GECCO
2006
Springer
174views Optimization» more  GECCO 2006»
9 years 10 months ago
On the analysis of the (1+1) memetic algorithm
Memetic algorithms are evolutionary algorithms incorporating local search to increase exploitation. This hybridization has been fruitful in countless applications. However, theory...
Dirk Sudholt
GECCO
2006
Springer
181views Optimization» more  GECCO 2006»
9 years 10 months ago
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Trading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rule...
Harish Subramanian, Subramanian Ramamoorthy, Peter...
GECCO
2006
Springer
157views Optimization» more  GECCO 2006»
9 years 10 months ago
How randomized search heuristics find maximum cliques in planar graphs
Surprisingly, general search heuristics often solve combinatorial problems quite sufficiently, although they do not outperform specialized algorithms. Here, the behavior of simple...
Tobias Storch
GECCO
2006
Springer
179views Optimization» more  GECCO 2006»
9 years 10 months ago
Evolving cooperative behavior in a power market
This paper presents an evolutionary algorithm to develop cooperative strategies for power buyers in a deregulated electrical power market. Cooperative strategies are evolved throu...
Dipti Srinivasan, Dakun Woo, Lily Rachmawati, Kong...
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