Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. Current economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. In this paper we show th...

the multiple traveling salesman problem (mtsp) is a generalization of the famous traveling salesman problem (tsp), where more than one salesman is used in the solution. although the mtsp is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. this paper presents an efficient and evolutionary optimization algorith...

In many regression learning algorithms for fuzzy rule bases it is not possible to define the error measure to be optimized freely. A possible alternative is the usage of global optimization algorithms like genetic programming approaches. These approaches, however, are very slow because of the high complexity of the search space. In this paper we present a novel approach where we first create a ...

This paper investigates the use of evolutionary algorithms for the search of hypothesis space in machine learning tasks. As opposed to the common scalar evaluation function imposing a complete order onto the hypothesis space, we propose genetic search incorporating pairwise comparison of hypotheses. Particularly, we allow incomparability of hypotheses, what implies a partial order in the hypoth...

Attribute estimation is an important machine learning problem. It is contained in many tasks, e.g., feature subset selection, constructive induction, decision and regression tree building. Relief algorithms are one of the most successful heuristic measures for solving this problem. The quality estimates of the Relief algorithms have commonly been interpreted as the difference of two probabiliti...

This paper introduces a software tool named KEEL, which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with dif...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

The credit derivatives market has experienced remarkable growth over the past decade. As such, there is a growing interest in tools for pricing of the most prominent credit derivative, the credit default swap (CDS). In this paper, we propose a heuristic algorithm for pricing of basket default swaps (BDS). For this purpose, genetic network programming (GNP), which is one of the recent evolutiona...