Evidence of coevolution in multi-objective evolutionary algorithms

This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can drive coevolutionary processes; a conclusion that mirrors arguments put forth in dual phase evolution theory. In the discussion, we briefly consider how our results may shed light onto this and other recent theories of evolution

A Case for Reimagining Reflection-in-Action and Co-evolution

This paper discusses core aspects of “Reflection-in-Action” found in the work of Schon and others suggesting that fundamentally an individual designer draws upon their previous pattern of experience and knowledge responding to complexity in a spontaneous tacit way. It is argued this has some limitations. Moreover, it is argued the nature of the limitations may be owed to the fact designers may limit the field of issues and indeed the dynamic interplay of the relationships both among and within issues and indeed contexts. Afterward, the paper draws upon the ideas of co-evolution found within Maher and Poon (1996) and Dorst & Cross (2001) suggesting the way the co-evolution model is often interpreted may also be somewhat limiting, as designers appear to “Muddle Through” a design problem co-evolving the problem and solution. Given these limitations, a reimagining of these models is presented. This paper supports the case that forestalling solution development in order to focus on developing a well-considered and comprehensively mapped Problem space first holds immense value for the creative design thinking process

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Impact of cognitive load associated with learning and using parametric tools in architectural design

This research aims to explore the impact of cognitive load associated with parametric tools on design ideation. Cognitive Load Theory refers to leveraged resources in limited working memory. In design, benefits have been found in higher load situations. However, semantic processing, associated with learning processes, has shown negative impact on the design outcome. Because of the rapid evolution of software, computational expertise tends to be increasingly transient, and architects find themselves in a situation where they constantly must partially re-learn their tools. Only few research takes the mental activity associated with digital environments into account, especially more complex ones such as parametric. Furthermore, there is no trace of research regarding how mental load associated with learning can affect design production. This paper focuses on an elective master course on computational design for architects. Both retrospective and concurrent protocol analysis are used in combination with the function behaviour structure ontology and linkography We observe that most of the cognitive effort is geared towards resolving issues related to using parametric tools, which is contradictory to previous studies. We find that their use of over-constrained experimental environments does not enable them to capture the learning related cognitive activity. Thus, it raises the question of experimental settings and research methodology regarding cognition in the digital age

IGATY: an archetype-based interactive generative abstraction system focusing on museum interior archetypes

Archetype in Greek means an original model that prevails in all later forms of variations, combinations, and transformations. In the field of design, types and archetypes have been used as an analytical tool; unfortunately, archetypes have not been perceived as promising prospects in the search for creative ideas, and the dynamic transformative quality embedded in archetypes has not been fully utilized among students and designers. Despite its inherent potential as sources of ideas for future invention, a number of scholars have criticized the typological approach to design for its exclusive nature primarily due to a misunderstanding of its fundamental structure. This dissertation aims at clarifying this misconception and explores a method that involves taking advantage of the malleable structure of archetypes. In Part 1 of this dissertation, I redefine the malleable structure of archetypes as a dual structure in which two contrasting yet equally crucial elements coexist: a core signal and a set of peripherals. The study focuses on verification of this dual structure and identification of core signals and peripherals in the six selected museum interior archetypes as a test set. In Part 2 I explore the archetype’s transformative quality using the interactive genetic algorithm (IGA). The dual structure of museum interior archetypes defined in Part 1 was mapped into the genetic algorithms to design an archetype-based generative abstraction system integrated with the Unity game engine, named IGATY-beta. The focus was to develop a system that would serve as an interactive ideation partner, not as a single-solution-oriented optimization tool. In Part 3 a quasi-experiment was conducted to examine the proposed IGATY-beta system’s educational potential in enhancing creativity in the ideation process. Three teaching scenarios based on three instructional materials were compared: (a) manual sketch-based archetypes exercise; (b) archetypes exercise using the IGATY-beta system displayed on a computer screen; and (c) archetypes exercise using the IGATY-beta system with an opportunity of viewing design in a virtual environment via a HMD. The results suggest the proposed archetype-based generative abstraction system’s positive educational potentials in enhancing creativity in the ideation process. Finally, the implications of the proposed generative abstraction system in the field of design are discussed

Virtual player design using self-learning via competitive coevolutionary algorithms

The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.This work is partially supported by Spanish MICINN under Project ANYSELF (TIN2011-28627-C04-01),3 by Junta de Andalucía under Project P10-TIC-6083 (DNEMESIS) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

EXPLOITING KASPAROV'S LAW: ENHANCED INFORMATION SYSTEMS INTEGRATION IN DOD SIMULATION-BASED TRAINING ENVIRONMENTS

Despite recent advances in the representation of logistics considerations in DOD staff training and wargaming simulations, logistics information systems (IS) remain underrepresented. Unlike many command and control (C2) systems, which can be integrated with simulations through common protocols (e.g., OTH-Gold), many logistics ISs require manpower-intensive human-in-the-loop (HitL) processes for simulation-IS (sim-IS) integration. Where automated sim-IS integration has been achieved, it often does not simulate important sociotechnical system (STS) dynamics, such as information latency and human error, presenting decision-makers with an unrealistic representation of logistics C2 capabilities in context. This research seeks to overcome the limitations of conventional sim-IS interoperability approaches by developing and validating a new approach for sim-IS information exchange through robotic process automation (RPA). RPA software supports the automation of IS information exchange through ISs’ existing graphical user interfaces. This “outside-in” approach to IS integration mitigates the need for engineering changes in ISs (or simulations) for automated information exchange. In addition to validating the potential for an RPA-based approach to sim-IS integration, this research presents recommendations for a Distributed Simulation Engineering and Execution Process (DSEEP) overlay to guide the engineering and execution of sim-IS environments.Major, United States Marine CorpsApproved for public release. Distribution is unlimited

An exploration of evolutionary computation applied to frequency modulation audio synthesis parameter optimisation

With the ever-increasing complexity of sound synthesisers, there is a growing demand for automated parameter estimation and sound space navigation techniques. This thesis explores the potential for evolutionary computation to automatically map known sound qualities onto the parameters of frequency modulation synthesis. Within this exploration are original contributions in the domain of synthesis parameter estimation and, within the developed system, evolutionary computation, in the form of the evolutionary algorithms that drive the underlying optimisation process. Based upon the requirement for the parameter estimation system to deliver multiple search space solutions, existing evolutionary algorithmic architectures are augmented to enable niching, while maintaining the strengths of the original algorithms. Two novel evolutionary algorithms are proposed in which cluster analysis is used to identify and maintain species within the evolving populations. A conventional evolution strategy and cooperative coevolution strategy are defined, with cluster-orientated operators that enable the simultaneous optimisation of multiple search space solutions at distinct optima. A test methodology is developed that enables components of the synthesis matching problem to be identified and isolated, enabling the performance of different optimisation techniques to be compared quantitatively. A system is consequently developed that evolves sound matches using conventional frequency modulation synthesis models, and the effectiveness of different evolutionary algorithms is assessed and compared in application to both static and timevarying sound matching problems. Performance of the system is then evaluated by interview with expert listeners. The thesis is closed with a reflection on the algorithms and systems which have been developed, discussing possibilities for the future of automated synthesis parameter estimation techniques, and how they might be employed