GENETICA The problem-solving approach proposed here, largely based on the previous remarks, is implemented through the computer language GENETICA. GENETICA is integrated in a programming environment that includes an evolutionary computational system.

The respective data-generation scenarios are evaluated with respect to either confirmation or optimization goals formulated in the program. Evaluation provides fitness values which are assigned to the genotypes. These decisions depend on genes both created and structured by the computational system at run-time. GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Genotypes New Data Generation Scenarios Fitness Structuring Genetic Operations Substitution Population Comparison Solution Given a GENETICA program, different executions lead to different data-generation scenarios. Differences are caused by the non deterministic elementary decisions occurring during the execution. Each gene-structure constitutes genotype of a data-generation scenario. Different program executions result in a population of genotypes. The computational system evolves the population by performing genetic operations on high fitness genotypes, … … realizing the data-generation scenarios defined by the resulting genotypes, … … estimating their fitness, …… and substituting low-fitness genotypes in the population. The best fitness genotype after the evolution procedure, defines the data-generation scenario that produces the solution. The solution is a data structure constructed by a specific formula within the program.

GENETICA includes predicate logic, while provides high-order modes of expression by allowing formulas to be treated as terms. Atomic terms are integers, reals and symbols, while non atomic terms are lists. All the modes of expression practically used in computer languages are either included or can be constructed in GENETICA, while practically all kinds of data structures can be represented as nested lists. BENEFITS OF GENETICA-BASED PROBLEM SOLVING Expressiveness in problem representation Creativity Combined confirmation & optimization goals Development of domain-specific languages and implementation of Genetic Programming Considering the main features of evolutionary methods, the proposed approach could offer the following benefits: The problem formulation is supported by the expressive power of formal logic. As a consequence, formally expressed knowledge can be incorporated in the problem formulation allowing an accurate, arbitrarily sophisticated problem statement and search space organization. Involvement of formal logic does not limit creativity: existing knowledge, specifying the domain of evolving entities, can be formulated in terms of formal logic, while novel features of these entities emerge by evolution. The problem-solving method can cope with general confirmation problems, as well as problems combining both confirmation and optimization goals. An important aspect of the proposed approach is the possibility to consider evolving data structures as computer programs written in languages developed in GENETICA. This makes possible to develop GP methods where problem specific knowledge could be fully incorporated in the problem representation.

GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Genotypes New Data Generation Scenarios Fitness Structuring Genetic Operations Substitution Population Comparison Solution Note that in the flowchart presented before …

GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Genotypes New Data Generation Scenarios Fitness Genetic Operations Substitution Population Solution Structuring Comparison … some procedures (marked in blue) are typical in any kind of computer-aided problem-solving, …

GenesElementary decisions Data Generation Scenarios Fitness New Genotypes New Data Generation Scenarios Fitness Genetic Operations Substitution Population Solution Structuring Comparison Genotypes … while others (marked in green) are typical in evolutionary methods.In order to implement the proposed approach, the following questions should be answered:

GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Data Generation Scenarios Fitness Genetic Operations Substitution Population Solution Structuring Comparison New Genotypes how elementary decisions can be determined by genes, …

GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Data Generation Scenarios Fitness Genetic Operations Substitution Population Solution Structuring Comparison New Genotypes how these genes can be structured in genotypes determining data- generation scenarios, …

GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Data Generation Scenarios Fitness Genetic Operations Substitution Population Solution Structuring Comparison New Genotypes what kind of genetic operations can be applied on these genotypes, …

GenesElementary decisions Genotypes Data Generation Scenarios Fitness New Data Generation Scenarios Fitness Genetic Operations Substitution Population Solution Structuring Comparison New Genotypes … and how fitness can be estimated on data-generation scenarios, especially with respect to confirmation goals.

Continued in Section_3.PPS