GECCO
2007
Springer
13 years 11 months ago
2007
Springer
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
This paper introduces a method for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring clusters. The proposed ...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
This paper presents a novel approach to clustering using an accuracy-based Learning Classifier System. Our approach achieves this by exploiting the generalization mechanisms inher...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
A common problem in genetic programming search algorithms is destructive crossover in which the offspring of good parents generally has worse performance than the parents. Design...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
Existing metrics for dynamic optimisation are designed primarily to rate an algorithm’s overall performance. These metrics show whether one algorithm is better than another, but...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
When facing dynamic optimization problems the goal is no longer to find the extrema, but to track their progression through the space as closely as possible. Over these kind of ov...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
The dynamic optimization problem concerns finding an optimum in a changing environment. In the tracking problem, the optimizer should be able to follow the optimum’s changes ov...
GECCO
2007
Springer
13 years 11 months ago
2007
Springer
Speciation is an important concept in evolutionary computation. It refers to an enhancements of evolutionary algorithms to generate a set of diverse solutions. The concept is stud...