Now showing items 1-10 of 20
Benchmark generator for CEC 2009 competition on dynamic optimization
(University of Leicester, U.K., 2008-10)
Benchmark generator for CEC 2009 competition on dynamic optimization. Technical Report 2008.
(Department of Computer Science, University of Leicester., 2008)
A multi-agent based evolutionary algorithm in non-stationary environments.
In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice ...
Population-based incremental learning with associative memory for dynamic environments.
In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown due to its importance in real-world applications. Several approaches, such as the memory and multiple ...
A generalized approach to construct benchmark problems for dynamic optimization.
There has been a growing interest in studying evolutionary algorithms in dynamic environments in recent years due to its importance in real applications. However, different dynamic test problems have been used to test and ...
An island based hybrid evolutionary algorithm for optimization.
Evolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially ...
Memory based on abstraction for dynamic fitness functions.
(Springer- Verlag., 2008)
This paper proposes a memory scheme based on abstraction for evolutionary algorithms to address dynamic optimization problems. In this memory scheme, the memory does not store good solutions as themselves but as their ...
A memetic algorithm for the university course timetabling problem.
The design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that ...
Evolutionary programming with q-Gaussian mutation for evolutionary optimization problems.
The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is ...
Learning in abstract memory schemes for dynamic optimization.
We investigate an abstraction based memory scheme for evolutionary algorithms in dynamic environments. In this scheme, the abstraction of good solutions (i.e., their approximate location in the search space) is stored in ...