text: In a steady-state evolution, tournament selection traditionally uses the fitness function to select the parents, and negative selection chooses an individual to be replaced with an offspring. This contribution focuses on analyzing the behavior, in terms of performance, of different heuristics when used instead of the fitness function in tournament selection. The heuristics analyzed are related to measuring the similarity of the individuals in the semantic space. In addition, the analysis includes random selection and traditional tournament selection. These selection functions were implemented on our Semantic Genetic Programming system, namely EvoDAG, which is inspired by the geometric genetic operators and tested on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of accuracy and the random selection, in the negative tournament, produces the best combination, and the difference in performances between this combination and the tournament selection is statistically significant. Furthermore, we compare EvoDAG's performance using the selection heuristics against 18 classifiers that included traditional approaches as well as auto-machine-learning techniques. The results indicate that our proposal is competitive with state-of-art classifiers. Finally, it is worth to mention that EvoDAG is available as open source software.
  @cite_28: This paper investigates the efficiency of using semantic and syntactic distance metrics in fitness sharing with Genetic Programming (GP). We modify the implementation of fitness sharing to speed up its execution, and used two distance metrics in calculating the distance between individuals in fitness sharing: semantic distance and syntactic distance. We applied fitness sharing with these two distance metrics to a class of real-valued symbolic regression. Experimental results show that using semantic distance in fitness sharing helps to significantly improve the performance of GP more frequently, and results in faster execution times than with the syntactic distance. Moreover, we also analyse the impact of the fitness sharing parameters on GP performance helping to indicate appropriate values for fitness sharing using a semantic distance metric.
  @cite_29: Semantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the 'black box' of the evaluation function and make explicit use of more information on program behavior in the search. In the most common scenario of evaluating a GP program on a set of input-output examples (fitness cases), the semantic approach characterizes program with a vector of outputs rather than a single scalar value (fitness). The past research on semantic GP has demonstrated that the additional information obtained in this way facilitates designing more effective search operators. In particular, exploiting the geometric properties of the resulting semantic space leads to search operators with attractive properties, which have provably better theoretical characteristics than conventional GP operators. This in turn leads to dramatic improvements in experimental comparisons. The aim of the tutorial is to give a comprehensive overview of semantic methods in genetic programming, illustrate in an accessible way a formal geometric framework for program semantics to design provably good mutation and crossover operators for traditional GP problem domains, and to analyze rigorously their performance (runtime analysis). A number of real-world applications of this framework will be also presented. Other promising emerging approaches to semantics in GP will be reviewed. In particular, the recent developments in the behavioral programming, which aims at characterizing the entire program behavior (and not only program outputs) will be covered as well. Current challenges and future trends in semantic GP will be identified and discussed. Selected methods and concepts will be accompanied with live software demonstrations. Also, efficient implementation of semantic search operators may be challenging. We will illustrate very efficient, concise and elegant implementations of these operators, which are available for download from the web.
summary: Let us recall that Semantic GP uses the information in the target behavior, i.e., @math , to guide the search. Notably, Krawiec @cite_29 affirmed that aware semantic methods make search algorithms better informed. For example, Nguyen @cite_28 proposed Fitness Sharing, a technique that promotes dispersion and diversity of individuals. Their proposal consisted of calculating the individual fitness as @math , where @math is approximately equal to the number of individuals that behave similarly to individual @math .