Human-based computation

Abstract: A new class of genetic algorithms (GA) is presented. It is based on the idea of outsourcing, a popular trend in business. In a human based genetic algorithm (HBGA), all primary genetic operators are outsourced, i.e. delegated to external human agents. A totally outsourced genetic algorithm uses both human evaluation and the human ability of innovation. It is a multi-agent environment and the mediator of communication between multiple heterogenous agents. The advantage of this approach is its ability to address complex problems for which it is hard, not only to evaluate individuals, but to find a good representation for them. These qualities allow HBGA to process flows of information without knowledge of its particular structure and representation. The suggested conceptual approach can also be used as a general model and a way of thinking about different kinds of genetic algorithms.

Abstract: Evolutionary computation has experienced a tremendous growth in the last decade in both theoretical analyses and industrial applications. Its scope has evolved beyond its original meaning of “biological evolution” toward a wide variety of nature inspired computational algorithms and techniques, including evolutionary, neural, ecological, social and economical computation, etc., in a unified framework. Many research topics in evolutionary computation nowadays are not necessarily “evolutionary”. This paper provides an overview of some recent advances in evolutionary computation that have been made in CERCIA at the University of Birmingham, UK. It covers a wide range of topics in optimization, learning and design using evolutionary approaches and techniques, and theoretical results in the computational time complexity of evolutionary algorithms. Some issues related to future development of evolutionary computation are also discussed.

Abstract: This paper examines the limitations of Turing Machines as a complete model of computation, and presents several models that extend Turing Machines. Dynamic interaction of clients and servers on the Internet, an infinite adaptation from evolutionary computation, and robots sensing and acting are some examples of areas that cannot be properly described using Turing Machines and algorithms. They require new models of computation going beyond Turing Machines. We refer to such new models as superTuring models of computation. Three superTuring models of computation, namely Interaction Machines, the π calculus and the $-calculus are presented and explained, focussing on why they are better for the solution of computational problems. We expect that superTuring computation will become the central programming paradigm in the future.