Phi-array: A novel method for fitness visualization and decision making in evolutionary design optimization

Mourshed, Monjur; Shikder, Shariful H.; Price, Andrew D.F.

doi:10.1016/j.aei.2011.07.005

Cited by 19 publications

(5 citation statements)

References 28 publications

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“…Regardless of the perspective, the choice of the visualization method heavily depends on the solution encoding. Some often used representations are simple "zebras" and Gonzo's search space view for binary encodings [11,22,71], parallel coordinates and heat maps (also called matrix charts or density plots) for realvalued genotypes [23], graphs and trees for discrete optimization problems [12,61], radial trees for genetic programming [13,26], and domain-specific representations for some real-world problems [21,38,50,63]. In multi-and many-objective problems, the focus of the analysis, and consequently visualization, shifts to the objective space and the challenge of visualizing high-dimensional Pareto front approximations [62].…”

Section: Related Workmentioning

confidence: 99%

Unveiling evolutionary algorithm representation with DU maps

Medvet

Virgolin

Castelli

et al. 2018

Genet Program Evolvable Mach

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Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly available to all users, or too general to convey detailed information. In this work, we study the Diversity and Usage map (DU map), a compact visualization for analyzing a key component of every EA, the representation of solutions. In a single heat map, the DU map visualizes for entire runs how diverse the genotype is across the population and to which degree each gene in the genotype contributes to the solution. We demonstrate the generality of the DU map concept by applying it to six EAs that use different representations (bit and integer strings, trees, ensembles of trees, and neural networks). We present the results of an online user study about the usability of the DU map which confirm the suitability of the proposed tool and provide important insights on our design choices. By providing a visualization tool that can be easily tailored by specifying the diversity (D) and usage (U) functions, the DU map aims at being a powerful analysis tool for EAs practitioners, making EAs more transparent and hence lowering the barrier for their use.

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Section: Related Workmentioning

confidence: 99%

Unveiling evolutionary algorithm representation with DU maps

Medvet

Virgolin

Castelli

et al. 2018

Genet Program Evolvable Mach

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“…609154. The scalarization of objective functions is further discussed in Mourshed (2006), Mourshed et al (2011). Another approach is to find a set of trade-off solutions that represents the best compromise between the objectives.…”

Section: Acknowledgementsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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“…However, with the increase in the number of design variables (i.e., factors or aspects) and goals, the reconciliation between conflicts becomes complicated, rendering the conventional 'cognitive' and 'trial and error' approach inefficient for effective decision-making (Mourshed, 2006). To overcome the limitations of cognitive or heuristics based approaches, design automation such as numerical optimization (Caldas, 2008;Mourshed et al, 2011) can be applied where the indices of design factors and their relative ranking are converted into proportional weights on design goals and the solution space is searched algorithmically.…”

Section: Integrating User Perception In Healthcare Facility Designmentioning

confidence: 99%