PPSN 2004 - 3rd session
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The document summarizes several papers presented at a conference on evolutionary computation and representations. It provides brief 1-2 sentence summaries of the main topics, objectives, and conclusions of 14 different papers presented at the conference. The papers covered a wide range of topics including ant algorithms, genetic programming, evolutionary multi-objective optimization, reinforcement learning, and hybrid evolutionary algorithms.
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the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
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Optimizing search via diversity enhancement in evolutionary MasterMind
A MasterMind player must discover a secret
combination by making guesses using the hints obtained as
a response to the previous ones. Finding a general strategy
that scales well with problem size is still an open issue, despite
having been approached from different angles, including evolu-
tionary algorithms. In previous papers we have tested different
approaches to the evolutionary MasterMind and having found
out that diversity is essential in this kind of combinatorial
optimization problems, in this paper we try to tune the search
methods to keep a high diversity level and thus obtain solutions
to the puzzle in less average evaluations, and, if possible, in
less number of combinations played. This will allow us to get
improvements in the time that will be used to explore problems
of bigger size.
Paper presented at the ICCS'12-Agadir conference
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Parallel Problem Solving from Nature session-4
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An Algorithm for solving the game of Mastermind
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The L-Co-R co-evolutionary algorithm: a comparative analysis in medium-term t...
The document describes a study comparing the L-Co-R co-evolutionary algorithm to other forecasting methods for medium-term time-series problems. The L-Co-R algorithm uses coevolving radial basis function neural networks (RBFNNs) and time lags to make predictions. The study uses several Spanish economic and transportation datasets and evaluates the accuracy of L-Co-R versus exponential smoothing, Croston's method, Theta, random walk, and ARIMA models based on mean absolute percentage error, mean absolute scaled error, and median absolute percentage error. Preliminary results show that L-Co-R performs well in predicting airline passenger numbers compared to other methods.
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Improving and Scaling Evolutionary Approaches to the MasterMind Problem
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8º hackatón de proyectos libres de la UGR: Ayuda para los participantes
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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PPSN 2004 - 3rd session
- 1. Session 3 General Evolutionary Computation and representations... Mostly Chairperson: J. J. Merelo
- 2. Broadcast Ants GP-Feature creation
- 21. http://www.flickr.com Upload your pictures and use tag PPSN
- 22. Go and posterize! Thank you!