Genetic Programming and Evolvable Machines 

About: Genetic Programming and Evolvable Machines is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Genetic programming & Evolutionary algorithm. It has an ISSN identifier of 1389-2576. Over the lifetime, 507 publications have been published receiving 16619 citations.

Compositional pattern producing networks: A novel abstraction of development

Abstract: Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.

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.

Principles in the Evolutionary Design of Digital Circuits—Part II

Abstract: In a previous work it was argued that by studying evolved designs of gradually increasing scale, one might be able to discern new, efficient, and generalisable principles of design. These ideas are tested in the context of designing digital circuits, particularly arithmetic circuits. This process of discovery is seen as a principle extraction loop in which the evolved data is analysed both phenotypically and genotypically by processes of data mining and landscape analysis. The information extracted is then fed back into the evolutionary algorithm to enhance its search capabilities and hence increase the likelihood of identifying new principles which explain how to build systems which are too large to evolve.

Evolutionary computation: a unified approach

Abstract: While Lawrence Fogel, John Holland, Ingo Rechenberg and others were the undoubted pioneers of the field we now know as evolutionary algorithms (EA), or evolutionary computation (EC), Ken De Jong’s doctoral thesis of 1975 deserves much of the credit for firing the enthusiasm of several research communities in the practical exploration of these methods. Moreover, as he has taken a very active part in the development of the field through the last 30 years, there could scarcely be anyone better placed to write a book on evolutionary computation. As the subtitle of his book promises, De Jong takes a unified approach. His first 4 chapters carefully explain and differentiate, whilst putting in their historical context, the common aspects of different EC paradigms (evolutionary programming—EP, evolution strategies—ES and genetic algorithms—GA). Chapters 1–4 use clear examples, rather than too many mathematical symbols. They form a truly superb introduction. Any novice coming to EC should come away with an excellent grasp of the basics. In chapter 5 he discusses the different uses to which EAs have been put as problem-solvers. The greater part is devoted to optimization (OPT-EA), with shorter sections on search, machine learning, and automated programming. There is a final, very brief, section on adaptive EAs. In the optimization part, considerable care is taken in the organisation of his material—again, presumably, with the novice in mind. Chapter 6 is the longest, and focuses on EC theory. De Jong carefully builds up a picture of the influences of selection, mutation and recombination on the behaviour of EAs. If you are expecting theory in the sense of a comprehensive, general model with well-understood effects, you will be disappointed. There are equations, but the argument is in fact founded on a series of experiments, whose results are displayed in a series of graphs. That is not to say that the insights gained are incorrect, or

Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

Abstract: This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.