Evolution strategies with subjective selection

2017

Artificial Life

Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives. It enables the exploitation of novel materials and processes without formal models or complex simulation. In this paper, we focus upon the coevolutionary nature of the design process when it is decomposed into concurrent sub-design threads due to the overall complexity of the task. Using an abstract, tuneable model of coevolution we consider strategies to sample sub-thread designs for whole system testing and how best to construct and use surrogate models within the coevolutionary scenario. Drawing on our findings, the paper then describes the effective design of an array of six heterogeneous vertical-axis wind turbines.

Section: Evolving Physical Systemsmentioning

confidence: 99%

On Design Mining: Coevolution and Surrogate Models

2017

Artificial Life

“…For example, the first ESs were used to design jet nozzles with a string of section diameters, which were then machined and tested for fitness (Rechenberg, 1971). Other well-known examples include robot controller design (Nolfi, 1992), electronic circuit design using programmable hardware (Thompson, 1998), product design via human provided fitness values (Herdy, 1996), chemical systems (Theis et al, 2006), and unconventional computers (Harding and Miller, 2004). More recently, Boria et al (2009) used an EA to evolve a morphing wing structure where physical designs were morphed using a set of actuators and evaluated in a closed-loop wind tunnel.…”

Section: Evolutionary Designmentioning

confidence: 99%

Design Mining Interacting Wind Turbines

Evolutionary Computation

2016

An initial study of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer has recently been presented. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined

“…For example, the first evolution strategies were used to design jet nozzles with a string of section diameters, which were then machined and tested for fitness [63]. Other well-known examples include robot controller design [54], electronic circuit design using programmable hardware [77], product design via human provided fitness values [33], chemical systems [76], and unconventional computers [30]. More recently, [9] used an EA to evolve a morphing wing structure where physical designs were morphed using a set of actuators and evaluated in a closed-loop wind tunnel.…”

Section: Evolving Physical Artefactsmentioning

confidence: 99%

Towards the evolution of vertical-axis wind turbines using supershapes

2014

Evol. Intel.

We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under fan generated wind conditions after being physically instantiated by a 3D printer. That is, unlike other approaches such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made. However, the representation used significantly restricted the range of morphologies explored. In this paper, we present initial explorations into the use of a simple generative encoding, known as Gielis superformula, that produces a highly flexible 3D shape representation to design VAWT. First, the target-based evolution of 3D artefacts is investigated and subsequently initial design experiments are performed wherein each VAWT candidate is physically instantiated and evaluated under fan generated wind conditions. It is shown possible to produce very closely matching designs of a number of 3D objects through the evolution of supershapes produced by Gielis superformula. Moreover, it is shown possible to use artificial physical evolution to identify novel and increasingly efficient supershape VAWT designs.