Publication informationGenetic Programming and Evolvable Machines, 12 (2): 91-119Publisher Springer Abstract 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, resulting in two new crossover operators -Semantics Aware Crossover (SAC) and Semantic Similarity-based Crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with Standard Crossover (SC), Context Aware Crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC.

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Abstract-Research on semantics in Genetic Programming(GP) has increased over the last number of years. Results in this area clearly indicate that its use in GP considerably increases performance. Many of these semantic-based approaches rely on a trial-and-error method that attempts to find offspring that are semantically different from their parents over a number of trials using the crossover operator (crossover-semantics based -CSB). This, in consequence, has a major drawback: these methods could evaluate thousands of nodes, resulting in paying a high computational cost, while attempting to improve performance by promoting semantic diversity. In this work, we propose a simple and computationally inexpensive method, named semantics in selection, that eliminates the computational cost observed in CSB approaches. We tested this approach in 14 GP problems, including continuous-and discrete-valued fitness functions, and compared it against a traditional GP and a CSB approach. Our results are equivalent, and in some cases, superior than those found by the CSB approach, without the necessity of using a "brute force" mechanism.

Origami engineering—the practice of creating useful three-dimensional structures through folding and fold-like operations on two-dimensional building-blocks—has the potential to impact several areas of design and manufacturing. In this article, we study a new concept for a self-folding system. It consists of an active, self-morphing laminate that includes two meshes of thermally-actuated shape memory alloy (SMA) wire separated by a compliant passive layer. The goal of this article is to analyze the folding behavior and examine key engineering tradeoffs associated with the proposed system. We consider the impact of several design variables including mesh wire thickness, mesh wire spacing, thickness of the insulating elastomer layer, and heating power. Response parameters of interest include effective folding angle, maximum von Mises stress in the SMA, maximum temperature in the SMA, maximum temperature in the elastomer, and radius of curvature at the fold line. We identify an optimized physical realization for maximizing folding capability under mechanical and thermal failure constraints. Furthermore, we conclude that the proposed self-folding system is capable of achieving folds of significant magnitude (as measured by the effective folding angle) as required to create useful 3D structures.

The effects of neutrality on evolutionary search have been considered in a number of interesting studies, the results of which, however, have been contradictory. Some researchers have found neutrality to be beneficial to aid evolution whereas others have argued that the presence of neutrality in the evolutionary process is useless. We believe that this confusion is due to several reasons: many studies have based their conclusions on performance statistics (e.g., on whether or not a system with neutrality could solve a particular problem faster than a system without neutrality) rather than a more in-depth analysis of population dynamics, studies often consider problems, representations and search algorithms that are relatively complex and so results represent the compositions of multiple effects (e.g., bloat or spurious attractors in genetic programming), there is not a single definition of neutrality and different studies have added neutrality to problems in radically different ways. In this paper, we try to shed some light on neutrality by addressing these problems. That is, we use the simplest possible definition of neutrality (a neutral network of constant fitness, identically distributed in the whole search space), we consider one of the simplest possible algorithms (a mutation based, binary genetic algorithm) applied to two simple problems (a unimodal landscape and a deceptive landscape), and analyse both performance figures and, critically, population flows from and to the neutral network and the basins of attraction of the optima.