Evolutionary computation for dynamic optimization problems

This is an invited tutorial on "Evolutionary Computation for Dynamic Optimization Problems", which was given at the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013)

Evolutionary Computation for Dynamic Optimization Problems

This is an invited tutorial on "Evolutionary Computation for Dynamic Optimization Problems", which was given at the 2015 Genetic and Evolutionary Computation Conference (GECCO 2015).Many real-world optimization problems are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic optimization problems (DOPs) are challenging problems for researchers and practitioners in decision-making due to their nature of difficulty. Yet, they are important problems that decision-makers in many domains need to face and solve. Evolutionary computation (EC) is a class of stochastic optimization methods that mimic principles from natural evolution to solve optimization and search problems. EC methods are good tools to address DOPs due to their inspiration from natural and biological evolution, which has always been subject to changing environments. EC for DOPs has attracted a lot of research effort during the last twenty years with some promising results. However, this research area is still quite young and far away from well-understood. This tutorial aims to summarise the research area of EC for DOPs and attract potential young researchers into the important research area. It will provide an introduction to the research area of EC for DOPs and carry out an in-depth description of the state-of-the-art of research in the field regarding the following five aspects: benchmark problems and generators, performance measures, algorithmic approaches, theoretical studies, and applications. Some future research issues and directions regarding EC for DOPs will also be presented. The purpose is to (i) provide clear definition and classification of DOPs; (ii) review current approaches and provide detailed explanations on how they work; (iii) review the strengths and weaknesses of each approach; (iv) discuss the current assumptions and coverage of existing research on EC for DOPs; and (v) identify current gaps, challenges, and opportunities in EC for DOPs

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat