Introduction

Abstract

This chapter previews the underlying approaches comprising parity logic, fuzzy logic, and evolutionary computing. Parity logic centers on efficient binary computing through automatic Boolean differentiation and integration, fast and entropy preserving transforms of binary arrays for reversible or nondissipative computing, and a broad scope of Boolean feedback machines called parity logic engines (PLEs). Fuzzy logic, in turn, centers on the design of nonlinear dynamical predictor systems in terms of fuzzy cognitive maps (FCMs) which endorse and enhance causal reasoning in decision processes, cross-impact analysis, and complex expert systems. Parity logic engines include evolutionary genetic search and optimization, thereby extending the computational framework of genetic algorithms (GAs) with autogenetic algorithms (AGAs). Altogether, these approaches provide new ways for fast computing, intelligent information processing, and highly adaptive system design, i.e. one type of system fits a wide range of diverse but formally intimately related problems. This holds for parity logic engines, fuzzy cognitive maps, and autogenetic algorithms.