Combinational Circuit Design with Estimation of Distribution Algorithms

The authors introduce new approaches for the combinational circuit design based on Estimation of Distribution Algorithms. In this paradigm, the structure and data dependencies embedded in the data (population of candidate circuits) are modeled by a conditional probability distribution function. The new population is simulated from the probability model thus inheriting the dependencies. The authors explain the procedure to build an approximation of the probability distribution through two approaches: polytrees and Bayesian networks. A set of circuit design experiments is performed and a comparison with evolutionary approaches is reported.

From the Real Ant to the Artificial Ant

Biological studies highlighting the collective behavior of ants in fulfilling various tasks by using their complex indirect communication process have constituted the starting point for many physical systems and various ant colony algorithms. Each ant colony is considered as a superorganism which operates as a unified entity made up of simple agents. These agents (ants) interact locally with one another and with their environment, particularly in finding the shortest path from the nest to food sources without any centralized control dictating the behavior of individual agents. It is this coordination mechanism that has inspired researchers to develop plenty of metaheuristic algorithms in order to find good solutions for NP-hard combinatorial optimization problems. In this chapter, the authors give a biological description of these fascinating insects and their complex indirect communication process. From this rich source of inspiration for researchers, the authors show how, through the real ant, artificial ant is modeled and applied in combinatorial optimization, data clustering, collective robotics, and image processing.

Nature-Inspired Informatics for Telecommunication Network Design

The speedy pace of change in telecommunications and its ubiquitous presence have drastically altered the way people interact, impacting production, government, and social life. The infrastructure for providing telecommunication services must be continuously renewed, as innovative technologies emerge and drive changes by offering to bring new services to the end users. In this context, the problem of efficiently designing the underlying networks in order to satisfy different requirements while at the same time keeping the capital and operative expenditures bounded is of ever growing importance and actuality. Network design problems have many variations, depending on the characteristics of the technologies to be employed, as well as on the simplifying hypothesis that can be applied on each particular context, and on the planning horizon. Nevertheless, in most cases they are extremely complex problems, for which exact solutions cannot be found in practice. Nature-inspired optimization techniques (belonging to the metaheuristic computational methods) are important tools in these cases, as they are able to achieve good quality solutions in reasonable computational times. The objective of this chapter is to present a systematic review of nature-inspired techniques employed to solve optimization problems related to telecommunication network design. The review is aimed at providing an insight of different approaches in the area, in particular covering four main classes of applications: minimum spanning trees, reliable networks, local access network design and backbone location, and cellular and wireless network design. A large proportion of the papers deal with single objective models, but there is also a growing number of works that study multi-objective problems, which search for solutions that perform well in a number of different criteria. While genetic algorithms and other evolutionary algorithms appear most frequently, there is also significant research on other methods, such as ant colony optimization, particle swarm optimization, and other nature-inspired techniques.

Modelling Biological Processes Naturally using Systemic Computation

Natural systems provide unique examples of computation in a form very different from contemporary computer architectures. Biology also demonstrates capabilities such as adaptation, self-repair, and self-organisation that are becoming increasingly desirable for our technology. To address these issues a computer model and architecture with natural characteristics is presented. Systemic computation is Turing Complete; it is designed to support biological algorithms such as neural networks, evolutionary algorithms and models of development, and shares the desirable capabilities of biology not found in conventional architectures. In this chapter the authors describe the first platform implementing such computation, including programming language, compiler and virtual machine. They first demonstrate that systemic computing is crash-proof and can recover from severe damage. The authors then illustrate various benefits of systemic computing through several implementations of bio-inspired algorithms: a self-adaptive genetic algorithm, a bio-inspired model of artificial neural networks, and finally we create an “artificial organism” - a program with metabolism that eats data, expels waste, clusters cells based on data inputs and emits danger signals for a potential artificial immune system. Research on systemic computation is still ongoing, but the research presented in this chapter shows that computers that process information according to this bio-inspired paradigm have many of the features of natural systems that we desire.

AGE-P

Smart-appliance ensembles consist of intelligent devices that interact with each other and that are supposed to support their users in an autonomous, non-invasive way. Since both the number and the composition of the participating devices may spontaneously change at any time without any notice, traditional approaches, such as rule-based systems and evolutionary algorithms, are not appropriate mechanisms for their self-organization. Therefore, this chapter describes a new evolutionary framework, called appliancesgo- evolution platform (AGE-P) that accounts for the inherent system dynamics by distributing all data structures and all operations across all participating devices. This chapter illustrates the behavior of this framework by presenting several results obtained from simulations as well as real-world case studies.

Noble Ape's Cognitive Simulation

Inspired by observing bacterial growth in agar and by the transfer of information through simple agar simulations, the cognitive simulation of Noble Ape (originally developed in 1996) has defined itself as both a philosophical simulation tool and a processor metric. The Noble Ape cognitive simulation was originally developed based on diverse philosophical texts and in methodological objection to the neural network paradigm of artificial intelligence. This chapter explores the movement from biological observation to agar simulation through information transfer into a coherent cognitive simulation. The cognitive simulation had to be tuned to produce meaningful results. The cognitive simulation was adopted as processor metrics for tuning performance. This “brain cycles per second” metric was first used by Apple in 2003 and then Intel in 2005. Through this development, both the legacy of primitive agar information-transfer and the use of this as a cognitive simulation method raised novel computational and philosophical issues.

Genetic Programming for Robust Text Independent Speaker Verification

Robust Automatic Speaker Verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, Genetic Programming offers inherent feature selection and solutions that can be meaningfully analyzed, making it well suited for this task. This chapter introduces a Genetic Programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. Also presented are the effects of a simulated telephone network on classification results which highlight the principal advantage, namely robustness to both additive and convolutive noise.

Solving Complex Problems in Human Genetics using Nature-Inspired Algorithms Requires Strategies which Exploit Domain-Specific Knowledge

In human genetics the availability of chip-based technology facilitates the measurement of thousands of DNA sequence variations from across the human genome. The informatics challenge is to identify combinations of interacting DNA sequence variations that predict common diseases. The authors review three nature-inspired methods that have been developed and evaluated in this domain. The two approaches this chapter focuses on in detail are genetic programming (GP) and a complex-system inspired GP-like computational evolution system (CES). The authors also discuss a third nature-inspired approach known as ant colony optimization (ACO). The GP and ACO techniques are designed to select relevant attributes, while the CES addresses both the selection of relevant attributes and the modeling of disease risk. Specifically, they examine these methods in the context of epistasis or gene-gene interactions. For the work discussed here we focus solely on the situation where there is an epistatic effect but no detectable main effect. In this domain, early studies show that nature-inspired algorithms perform no better than a simple random search when classification accuracy is used as the fitness function. Thus, the challenge for applying these search algorithms to this problem is that when using classification accuracy there are no building blocks. The goal then is to use outside knowledge or pre-processing of the dataset to provide these building blocks in a manner that enables the population, in a nature-inspired framework, to discover an optimal model. The authors examine one pre-processing strategy for revealing building blocks in this domain and three different methods to exploit these building blocks as part of a knowledge-aware nature-inspired strategy. They also discuss potential sources of building blocks and modifications to the described methods which may improve our ability to solve complex problems in human genetics. Here it is argued that both the methods using expert knowledge and the sources of expert knowledge drawn upon will be critical to improving our ability to detect and characterize epistatic interactions in these large scale biomedical studies.

An Artificial Life-Based Vegetation Modelling Approach for Biodiversity Research

The complexity of nature can only be solved by nature’s intrinsic problem-solving approach. Therefore, the computational modelling of nature requires careful observations of its underlying principles in order that these laws can be abstracted into formulas suitable for the algorithmic configuration. This chapter proposes a novel modelling approach for biodiversity informatics research. The approach is based on the emergence phenomenon for predicting vegetation distribution patterns in a multi-variable ecosystem where Artificial Life-based vegetation grow, compete, adapt, reproduce and conquer plots of landscape in order to survive their generation. The feasibility of the modelling approach presented in this chapter may provide a firm foundation not only for predicting vegetation distribution in a wide variety of landscapes, but could also be extended for studying biodiversity and the loss of animal species for sustainable management of resources.

Intelligent Business Process Execution using Particle Swarm Optimization

In this chapter, the authors study a new variant of Particle Swarm Optimization (PSO) to efficiently execute business processes. The main challenge of this application for the PSO is that the function evaluations typically take a high computation time. They propose the Gap Search (GS) method in combination with the PSO to perform a better exploration in the search space and study its influence on the results of our application. They replace the random initialization of the solutions for the initial population as well as for the diversity preservation method with the GS method. The experimental results show that the GS method significantly improves the quality of the solutions and obtains better results for the application as compared to the results of a standard PSO and Genetic Algorithms. Moreover, the combination of the methods the authors used show promising results as tools to be applied for improvement of Business Process Optimization.