Almost all existing genetic programming systems deal with fitness evaluation solely by testing. In this paper, by contrast, we present an original approach that combines genetic programming with Hoare logic with the aid of model checking and finite state automata, henceby proposing a brand new verification-focused formal genetic programming system that makes it possible to evolve reliable programs with mathematicallyverified properties.
Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualizing concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of Communicated by V. Loia. B Pei He an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated.
Twelve years have passed since the advent of grammatical evolution (GE) in 1998, but such issues as vast search space, genotypic readability, and the inherent relationship among grammatical concepts, production rules and derivations have remained untouched in almost all existing GE researches. Model-based approach is an attractive method to achieve different objectives of software engineering. In this paper, we make the first attempt to model syntactically usable information of GE using an automaton, coming up with a novel solution called model-based grammatical evolution (MGE) to these problems. In MGE, the search space is reduced dramatically through the use of concepts from building blocks, but the functionality and expressiveness are still the same as that of classical GE. Besides, complex evolutionary process can visually be analyzed in the context of transition diagrams.
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