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We investigate the effects of semantically-based crossover operators in Genetic Programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as Semantic Equivalence and Semantic Similarity. These relations are used to guide variants of the crossover operator,(More)
In Genetic Programming (GP) and most of the other evolutionary computing approaches, the knowledge which is learned during the evolutionary processing is implicitly encoded in the population. In this research, we proposed a new approach for program synthesis – Program Evolution with Explicit Learning (PEEL), which learns and makes use of this knowledge(More)
Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and(More)
— In Evolutionary Computation, genetic operators, such as mutation and crossover, are employed to perturb individuals to generate the next population. However these fixed, problem independent genetic operators may destroy the sub-solution, usually called building blocks, instead of discovering and preserving them. One way to overcome this problem is to(More)
— Standard tree-based genetic programming suffers from a structural difficulty problem, in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure, or as a result of the numerical distribution of tree shapes. We(More)
In this paper, we introduce a new grammar guided genetic programming system called tree-adjoining grammar guided genetic programming (TAG3P+), where tree-adjoining grammars (TAGs) are used as means to set language bias for genetic programming. We show that the capability of TAGs in handling context-sensitive information and categories can be useful to set a(More)
Knowing the estimated cost of a software project early in the development cycle is a valuable asset for management. In this paper, an evolutionary computation method, Grammar Guided Genetic Programming (GGGP), is used to t models, with the aim of improving the prediction of software development costs. Valuable results are obtained, signiicantly better than(More)
The main focus of this paper is to propose integration of dynamic and multiobjective algorithms for graph clustering in dynamic environments under multiple objectives. The primary application is to multiobjective clustering in social networks which change over time. Social networks, typically represented by graphs, contain information about the relations(More)