SEAL 2010

Eighth International Conference on

Simulated Evolution And Learning (SEAL-2010)
01 - 04 December 2010, Indian Institute of Technology Kanpur, India
 
   
 
Evolutionary computation and learning approaches are two fundamental methods of adaptation which received an increasing attention over the past years. Simulated Evolution and Learning (SEAL-2010) is the eighth biennial conference which will be followed after a series of successful SEAL conferences, the last being organized at the University of Melbourne, Australia in December 2008. Cross-fertilisation between evolutionary learning and other machine learning approaches, such as neural network learning, reinforcement learning, decision tree learning, fuzzy system learning, etc., will be encouraged by the conference. The other major theme of the conference is optimisation by evolutionary approaches or hybrid evolutionary approaches. The topics of interest to this conference include but are not limited to the following: Evolutionary Learning; Evolutionary Optimisation (single and multi-objective); Hybrid Learning; Hybrid optimization, Adaptive Systems; Theoretical Issues in Evolutionary Computation; Real-World Applications of Evolutionary Computation and learning Techniques. See Call For Papers for further details.
 
 

 

The Eighth International Conference on Simulated Evolution And Learning (SEAL-2010)

01 -- 04 December, IIT Kanpur, India

A PDF version of the Call-For-Papers.

Paper Access Site: http://www.easychair.org/conferences/?conf=seal10


Aim and Scopes:

Simulated Evolution and Learning SEAL-2010  is the eighth biennial conference in the highly successful conference series that aims at exploring these two forms of adaptation and their roles and interactions in adaptive systems. Any paper involving evolution as a vehicle for adaptive and artificial problem solving tasks and any form of computational and machine learning procedure for developing and analyzing adaptive or artificial systems will be of interest to this conference. Cross-fertilisation between evolutionary learning and other machine learning approaches, such as neural network learning, reinforcement learning, decision tree learning, fuzzy system learning, etc., are encouraged by the conference. The other major theme of the conference is optimization problem solving by evolutionary approaches or hybrid evolutionary approaches. The topics of interest to this conference include but are not limited to the following: Evolutionary Learning; Evolutionary Optimization (single and multi-objective); Hybrid Learning; Hybrid Optimization, Adaptive Systems; Theoretical Issues in Evolutionary Computation; Real-World Applications of Evolutionary Computation and Learning Techniques.

1. Evolutionary Optimisation
  • Numerical/Function Optimization
  • Combinatorial Optimization
  • Hybrid Optimization Algorithms
  • Comparison of Algorithms
  • Multi-objective optimization and decision making
  • Nature-Inspired Algorithms (ant colony optimisation, particle swarm optimisation, memetic algorithms, differential evolution, simulated annealing, etc.)
2. Evolutionary Learning
  • Fundamental Issues in Evolutionary Learning
  • Co-Evolutionary Learning
  • Modular Evolutionary Learning Systems
  • Classifier System
  • Collective Intelligence
  • Representation Issues in Evolutionary Learning
  • Artificial Immune Systems
  • Interactions Between Learning and Evolution
  • Credit Assignment
  • Swarm Intelligence
  • Comparison between Evolutionary Learning and Other Learning Approaches
3. Hybrid Learning
  • Evolutionary Artificial Neural Networks
  • Evolutionary Fuzzy Systems
  • Evolutionary Reinforcement Learning
  • Evolutionary Clustering
  • Evolutionary Decision Tree Learning
  • Evolutionary Unsupervised Learning
  • Genetic Programming
  • Other Hybrid Learning Systems
  • Developmental Processes
4. Adaptive Systems
  • Complexity in Adaptive Systems
  • Evolutionary Robotics
  • Evolvable Hardware and Software
  • Artificial Ecology
  • Evolutionary Games
  • Self-Repairing Systems
  • Evolutionary Computation Techniques in Economics, Finance and Marketing
5. Theoretical Issues in Evolutionary Computation
  • Computational Complexity of Evolutionary Algorithms
  • Self-Adaptation in Evolutionary Algorithms
  • Convergence and Convergence Rate of Evolutionary Algorithms
6. Real-World Applications of Evolutionary Computation and Learning Techniques

Publications:

All accepted papers which are presented at the conference will be included in the conference proceedings, published as a volume of the series Lecture Notes in Computer Science, Springer. Selected best papers will be invited for further revisions and extensions for possible publications by evolutionary computing related journals.

Important Dates:

26 July 2010:
Submission of full papers.
27 August 2010, Notification of acceptance.
03 September 2010, Deadline for camera-ready copies of accepted papers. At least one author of each accepted paper needs to register for the conference by 10 September 2010 to have the paper included in the Springer proceedings.
1-4 December 2010, Conference sessions (including tutorials and workshops)

 

 

 
 
 
Kanpur Genetic Algorithms Laboratory (KanGAL), SEAL-2010.