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Recent Developments in Biologically Inspired Computing is necessary reading for undergraduate and graduate students, and researchers interested in knowing the most recent advances in problem solving techniques inspired by nature. This book covers the most relevant areas in computational intelligence...

Biologically inspired computing is just one of the branches of natural computing, which also encompasses artificial life, fractal geometry and computing with natural means (molecular, membrane and quantum computing). This chapter provides a brief and general overview of natural computing, focusing on...

In this chapter we study the feasibility of using Turing Machines as a model for the evolution of computer programs. To assess this idea we select, as test problem, the Busy Beaver — a well-known theoretical problem of undisputed interest and difficulty proposed by Tibor Rado in 1962. We focus our...

In this chapter a recently proposed meta-heuristic devised to be used in complex optimization problems is presented. Called Generalized Extremal Optimization (GEO), it was inspired by a simple co-evolutionary model, developed to show the emergence of self-organized criticality in ecosystems. The...

In this chapter, a new representation scheme for Genetic Programming (GP) is proposed. We need a Turing-complete representation for a general method of generating programs automatically; that is, the representation must be able to express any algorithms. Our representation is a recurrent network...

In this chapter an artificial problem solver inspired in natural genotype/phenotype systems — gene expression programming — is presented. As an introduction, the fundamental differences between gene expression programming and its predecessors, genetic algorithms and genetic programming, are briefly...

The chapter describes the theory of clonal selection and its usage in designing and implementing immunological algorithms for problem solving and learning. In detail, it presents various immune algorithms based on the clonal selection principle, analyzing computational time complexity, experimental...

This chapter introduces two different ways to integrate Evolutionary Computation Components in Ant Colony Optimization (ACO) Meta-heuristic. First of all, the ACO meta-heuristic is introduced and compared to Evolutionary Computation to notice their similarities and differences. Then two new models of...

This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the...

Algorithms inspired by the collective behavior of social organisms, from insect colonies to human societies, promoted the emergence of a new field of research called swarm intelligence. The applications of swarm intelligence range from routing in telecommunication networks to robotics. This chapter...

Particle swarm optimization is a computer paradigm that is based on human social influence and cognition. Candidate problem solutions are randomly initialized, and improvements are found through interactions among them. Social-psychological aspects of the algorithm are described, followed by...

Here we propose, based on the Peircean semiotics and informed by neuroethological constraints, a methodology to simulate the emergence of symbolic predator-warning communication among artificial creatures in a virtual world of predatory events. In order to build a digital scenario, and infer the...

This chapter introduces the twin deadlocks of strong artificial life. Conceptualization of life is a deadlock both because of the existence of a continuum between the inert and the living, and because we only know one instance of life. Computationalism is a second deadlock since it remains a matter of...

Fractal proteins are a new evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot set. The resulting network of gene and protein interactions can be designed by evolution to produce specific...

The field of biologically inspired computing has generated many novel, interesting and useful computational systems. None of these systems alone is capable of approaching the level of behaviour for which the artificial intelligence and robotics communities strive. We suggest that it is now time to move...

In this chapter, we review our recent research in the area of collective robotics, and the problem of controlling multiple robots in the completion of common tasks. Our approach is characterized with a strong inclination for biological inspiration in which examples in nature — social insects in...

In this chapter a brief review is given of computational systems that are motivated by information processing in the brain, an area that is often called neurocomputing or artificial neural networks. While this is now a well studied and documented area, specific emphasis is given to a subclass of such...