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.
Pawlak @cite_8 proposed the Random Desired Operator (RDO). It propagates the target semantics to calculate the desired semantics in the node selected as mutation point. This desired behavior is used to search in a procedures library for the most similar subtree. Finally, it swaps the mutated node with the subtree. RDO was extended by Szubert @cite_0 introducing the Forward Propagation Mutation (FPM) which uses a combination of forward and back-propagation to find a combination of unitary and binary functions that is the most similar to the desired behavior.
@cite_0 @cite_8
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In recent years, a number of methods have been proposed that attempt to improve the performance of genetic programming by exploiting information about program semantics. One of the most important developments in this area is semantic backpropagation. The key idea of this method is to decompose a program into two parts—a subprogram and a context—and calculate the desired semantics of the subprogram that would make the entire program correct, assuming that the context remains unchanged. In this paper we introduce Forward Propagation Mutation, a novel operator that relies on the opposite assumption—instead of preserving the context, it retains the subprogram and attempts to place it in the semantically right context. We empirically compare the performance of semantic backpropagation and forward propagation operators on a set of symbolic regression benchmarks. The experimental results demonstrate that semantic forward propagation produces smaller programs that achieve significantly higher generalization performance. We propose Locally Geometric Crossover (LGX) for genetic programming. For a pair of homologous loci in the parent solutions, LGX finds a semantically intermediate procedure from a previously prepared library, and uses it as replacement code. The experiments involving six symbolic regression problems show significant increase in search performance when compared to standard subtree-swapping cross-over and other control methods. This suggests that semantically geometric manipulations on subprograms propagate to entire programs and improve their fitness. Inherent replica inconsistency refers to the difference among the replicas of the same logical data item in the write propagation process of a normally running distributed storage system. In this paper, we formalize the write propagation process model of Cassandra, a widely used NoSQL storage system. In the write propagation process we explore two queueing systems, sending task queues and mutation queues, which locate at each replica node and are determinants of the replica inconsistency. The departure time difference from the mutation queue is used as the measure of inconsistency between two replicas. Furthermore, Request Per Second (RPS) and Mutation Threads Number (MTN), which affect the inherent inconsistency, are discussed and the MTN adaptation algorithm is proposed. Finally, A Cassandra inconsistency measurement framework is implemented using the source instrumentation approach. The empirical results conform well with our proposed inconsistency measurement model. There is a great interest in the Genetic Programming (GP) community to develop semantic genetic operators. Most recent approaches includes the genetic programming framework for symbolic regression called Error Space Alignment GP, the geometric semantic operators, and our previous work the semantic crossover based on the partial derivative error. To the best of our knowledge, there has not been a semantic genetic operator similar to the point mutation. In this contribution, we start filling this gap by proposing a semantic point mutation based on the derivative of the error. This novel operator complements our previous semantic crossover and, as the results show, there is an improvement in performance when this novel operator is used, and, furthermore, the best performance in our setting is the system that uses the semantic crossover and the semantic point mutation.