Jonathan Timmis

Bio: Jonathan Timmis is an academic researcher. The author has contributed to research in topics: Computational intelligence & Immune network theory. The author has an hindex of 1, co-authored 1 publications receiving 1667 citations.

Artificial Immune Systems: A New Computational Intelligence Approach

Abstract: Introduction.- Fundamentals of the Immune System.- A Framework for Engineering Artificial Immune Systems.- A Survey of Artificial Immune Systems.- The Immune System in Context with Other Biological Systems.- AIS in Context with Other Computational Intelligence Paradigms.- Case Studies.- Conclusions and Future Trends.- References.- Appendix I: Glossary of Biological Terms.- Appendix II: Pseudocode for Immune Algorithms.- Appendix III: WEB Resources on Artificial Immune Systems. Index.

Learning and optimization using the clonal selection principle

Abstract: The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.

Solving Multiobjective Optimization Problems Using an Artificial Immune System

Abstract: In this paper, we propose an algorithm based on the clonal selection principle to solve multiobjective optimization problems (either constrained or unconstrained). The proposed approach uses Pareto dominance and feasibility to identify solutions that deserve to be cloned, and uses two types of mutation: uniform mutation is applied to the clones produced and non-uniform mutation is applied to the ?not so good? antibodies (which are represented by binary strings that encode the decision variables of the problem to be solved). We also use a secondary (or external) population that stores the nondominated solutions found along the search process. Such secondary population constitutes the elitist mechanism of our approach and it allows it to move towards the true Pareto front of a problem over time. Our approach is compared with three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimization. For our comparative study, three metrics are adopted and graphical comparisons with respect to the true Pareto front of each problem are also included. Results indicate that the proposed approach is a viable alternative to solve multiobjective optimization problems.

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Abstract: Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

Humans and Nature: How Knowing and Experiencing Nature Affect Well-Being

Abstract: Ecosystems provide many of the material building blocks for human well-being. Although quantification and appreciation of such contributions have rapidly grown, our dependence upon cultural connections to nature deserves more attention. We synthesize multidisciplinary peer-reviewed research on contributions of nature or ecosystems to human well-being mediated through nontangible connections (such as culture). We characterize these connections on the basis of the channels through which such connections arise (i.e., knowing, perceiving, interacting with, and living within) and the components of human well-being they affect (e.g., physical, mental and spiritual health, inspiration, identity). We found enormous variation in the methods used, quantity of research, and generalizability of the literature. The effects of nature on mental and physical health have been rigorously demonstrated, whereas other effects (e.g., on learning) are theorized but seldom demonstrated. The balance of evidence indicates conclusi...