Advances in Artificial Life and Evolutionary Computation: by Clara Pizzuti, Giandomenico Spezzano

By Clara Pizzuti, Giandomenico Spezzano

This publication constitutes the revised chosen papers of the ninth Italian Workshop on Advances in synthetic lifestyles and Evolutionary Computation held in Vietri sul Mare, Italy, in could 2014, along with the twenty fourth Italian Workshop on Neural Networks, WIRN 2014.

The sixteen papers awarded were completely reviewed and chosen from forty submissions. They disguise the next themes: man made neural networks; fuzzy inference structures; tough set; approximate reasoning; and optimization tools comparable to evolutionary computation, swarm intelligence, particle swarm optimization.

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As agents must not be eternal, 42 N. A. Pe˜ na an age-related death mechanism is introduced: the probability of dying increases with time. The question that arises at this point is: How are new agents generated to replace those who die? , how do they born? Instead of implementing complex and costly reproduction strategies, we decided to implement a strategy that periodically checks the conditions of an area in order to generate new agents that are adapted to live there. The simplest solution is to randomly select an area, compute its environmental state, compare this state with the profile of every available species and, finally, select one or more new creatures.

As illustrated in Figs. 4 and 5 the agents are not distributed Table 1. List of species and their characteristics used in the exemplar experimental simulation. Name Rhythm Emissions Requirements Tree Slow Medium elevation High humidity High vegetation Shrub Medium Medium vegetation Medium humidity High vegetation Cactus Medium Medium vegetation High temperature Low humidity Rabbit Fast — Medium elevation High vegetation Fig. 4. Example simulation: Three species populate the world during the transient initial phase shown in the figure at the right.

47–57, 2014. 1007/978-3-319-12745-3 5 48 D. Pacella et al. the problem of studying emotions in animals mainly lies in the difficulty of measuring and detecting variables. The methods used mostly incur into different kind of biases. In the case of the studies on the emotion of fear, for example, it is hard to isolate the correct trigger event which aroused the rat, and the measured variation of responses may be due to other factors, like daily and individual fluctuations. Also, autonomic responses are often similar among totally different kind of emotions: fear and sexual activity share the increase of cortisol level and cardiac frequency.

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