Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators

Abstract. In a series of papers [3-8], Daida et. al. highlighted the difficulties posed to Genetic Programming (GP) by the complexity of the structural search space, and attributed the problem to the expression tree representation in GP. In this paper, we show how to transform a fixed-arity expression tree in GP to a non fixed-arity tree (Catalan tree) using representation based on Tree Adjoining Grammars (TAGs). This non fixed-arity property, which is called feasibility, allows us to design many types of genetic operators (as in [16]). In particular, insertion/deletion operators arising naturally from the representation play a role as structural mutation operators. By using these dual operators on TAG-based representation, we demonstrate how these operators can help to soften the structural search difficulties in GP.

The Tree-String Problem: An Artificial Domain for Structure and Content Search

This paper introduces the Tree-String problem for genetic programming and related search and optimisation methods. To improve the understanding of optimisation and search methods, we aim to capture the complex dynamic created by the interdependencies of solution structure and content. Thus, we created an artificial domain that is amenable for analysis, yet representative of a wide-range of real-world applications

Selection enthusiasm.

This paper reports experimental results to test the hypothesis: does the technique change the overall fitness and diversity of Genetic Algorithms. An improved selection algorithm is reported in which a coefficient associated with an individual's fitness is adapted each time an individual is not selected to improve the probability of being selected the next time. The benchmark chosen was symmetric TSP and a new diversity metric was introduced. The results show that using such a technique did improve the overall diversity and fitness of GA. The work in continuing as part of a current PhD project

Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review

In 1996 Daida et al. reviewed the case for using symbiosis as the basis for evolving complex adaptive systems [6]. Specific observations included the impact of different philosophical views taken by biologists as to what constituted a symbiotic relationship and whether symbiosis represented an operator or a state. The case was made for symbiosis as an operator. Thus, although specific cost benefit characterizations may vary, the underlying process of symbiosis is the same, supporting the operator based perspective. Symbiosis provides an additional mechanism for adaption/ complexification than available under Mendelian genetics with which Evolutionary Computation (EC) is most widely associated. In the following we review the case for symbiosis in EC. In particular, symbiosis appears to represent a much more effective mechanism for automatic hierarchical model building and therefore scaling EC methods to more difficult problem domains than through Mendelian genetics alone