The Nature Inspired Creative Design network brings together people from three different main areas: Nature and Biology, Art and Design, and Science and Computing. It aims to establish a forum for crossfertilization between the disciplines, within the overall topic of adopting nature inspired approaches to creative design. Its members explore a wide range of subjects, including evolution, growth and development, emergence and self organization, robustness, natural structures, and human design behaviour and performance1. Nature Inspired Creative Design is a research cluster under the 'Designing for the 21st Century' program. It is sponsored by the British Arts and Humanities Research Council and the Engineering and Physical Sciences Research Council. The network brings together people from a variety of disciplines to exchange ideas about art, design, nature and science [Schnier06]. This chapter presents the background and ideas of the network, predominantly from a computational viewpoint. It also presents some of the result of the initial one-year funded phase of the network. 2. NATURE INSPIRED CREATIVE DESIGN Nature is the ultimate designer. Every species, every individual, can be seen as the result of an implicit design process. At the same time, the results of this design process perform extremely well, in whatever measure of performance one might want to use. This even often includes aesthetical measures – there are many extremely beautiful species on earth. The main goal of this network is to explore what can be learned from Nature for the human design and engineering activities. However, this is not a one-way process. The nature of the funding puts an emphasis on applications to design, and this chapter will maintain this emphasis. However, there is potential for transfer of ideas between all the groups participating in the network (Figure 1). Without this, the network would be one-sided and of questionable use to perhaps the majority of the members. 2.2 Promises of Nature Inspired Design The design process is becoming increasingly complex and demanding, and will continue to do so into the 1 This is an extended version of a paper originally published at ACDM 2006 (IP-CC, Bristol) future. Individual designs are composed of more and more parts; designs are subject to more and more constraints and objectives; at the same time the rapid technical innovations and shorter lifespans increase the demand for design. By adopting nature inspired methods, we hope to solve some of these problems. Nature inspired methods have a number of important potentials, as described in the following sections. 2.2.1 Produce better designs Comparing natural and artificial designs, it is clear that natural designs often have a number of advantages. For example, natural designs are usually: • Resource efficient: Through continuous adaptation, natural designs have become parsimonious in their use of resources. • Resilient to faults: Artificial designs often fail when individual components break. Natural designs, on the other hand, usually show graceful degradation: a small injury is usually not fatal to an animal, and the death of a number of individuals will not seriously endanger a colony of insects. • Adaptable: Natural designs are usually able to adapt to the environment; sometimes this happens as part of the development process from genetic code to individual, sometimes this happens during the lifetime as physical change, or as behavioural change. • Extremely varied: Natural design processes have produced individuals that span a massive range of scales, complexities, shapes and forms. They can be found in a vast range of environments. Nature inspired processes can help designers to create very novel designs. 2.2.2 Create better design processes By taking inspiration from nature's methods, we will be able to find processes that are Figure 1: Nature Inspired Creative Design Network • More scalable: Nature has developed systems that show a very high complexity: human nervous systems, colonies of insects, large ecosystems are all examples. Very often, these systems are composed of a large number of elements with complex interactions. Designing artificial systems of similar complexity is usually a very difficult process. • Better parallelizable: In natural evolution, there is no top-down design process – every species evolves on its own, and individual parts of a design (e.g. the beak of a Darwin Finch) can often be optimized fairly much independently of other parts. • More reliable: A very simple system consisting of 10 components interacting in 10 different ways has the state space of 10,000,000,000 possible states. A designer using conventional tools simply does not have the time to search the entire state space. Natural systems have developed various emergent methods for searching the sate space of the system. • More efficient. Natural design processes are unsupervised processes – no conscious designer is involved. Instead, processes of evolution, emergence, self-organization and interaction with the environment determine the outcome. Together with the increasing availability of fast computer clusters, nature inspired approaches have the potential to provide efficient alternatives to labour-intensive manual design processes. 2.2.3 Create better design tools Nature inspired design tools have the potential to • Allow the user to search a larger design space: designers are often limited to a small subset of the total design space, for a variety of reasons: lack of knowledge, lack of time, lack of design methods. Nature inspired methods may be able to help both producing designs in a larger search space, and evaluating these designs. • Provide better support: systems that know about the design process, and learn the user's preferences, will be able to provide better support to the designer. Nature inspired techniques can provide both languages to describe designs, as well as the learning algorithms. 2.2.4 Provide knowledge about human design activities • Humans are a part of nature. They are also a result of an evolutionary process – our artistic perception, and our creative abilities, are formed by this process. By studying aspects of human design activities, we can learn more about human and animal creative processes. Human creativity is a many-faceted process, and attempts at creating creative computer programs can benefit from emulating one or more of these aspects. Human creativity is also of course a result of human evolution. • Capture design knowledge. Human designers have developed a large pool of knowledge about design, both explicit and implicit. This knowledge can be applied in other disciplines, for example in data visualization. • Gain insights into ethical aspects of design. Natural life has survived many drastic changes, in terms of climate changes, natural disasters, or rapid evolutionary changes. Studying natural design dynamics may give us some ideas about possible interactions between the natural and human designed worlds. 3. AREAS OF INTEREST With about 40 members with a variety of backgrounds actively participating in the network, there are nearly as many subjects discussed. The following sections attempt to categorise and summarise these subjects.

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500-2250 r/min) and indicated mean effective pressures (IMEPs) (3-4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $\lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual.

Creativity is often described as the intelligent misuse or rearrangement of existing knowledge, and is meaningless without the context and associated expectations provided by that knowledge. We outline a model of problem-solving creativity based on the generalisation of cases and discuss how sub-symbolic computational intelligence techniques might be used to implement it. We propose a novel measurement for creativity based on the idea of creative potential or the ‘degree of misuse’ of existing concepts contained in a case base.

For Homogeneous Charge Compression Ignition (HCCI) combustion, the auto-ignition process is very sensitive to in-cylinder conditions. This includes the change in in-cylinder temperature, the composition of chemical components and their concentrations. This sensitivity presents a major challenge for the accurate control of reliable and efficient HCCI combustion. This paper outlines our recent work: 1. a real-time control oriented gasoline-fueled HCCI combustion model and its implementation in Simulink with fixed step for the conversion into dSPACE Hardware-in-the-Loop (HIL) simulation purpose. 2. The development of model-based fast calibration for the best fuel efficiency and hydrocarbon emissions via evolutionary algorithm (EA). The model reported in this paper is able to run in real-time cycle-to-cycle under engine speeds below 4000rpm and with fixed simulation steps. This aims to achieve cycle-to-cycle performance calculation for Model based Predictive Control (MPC) and HIL simulation. With the aim of controlling the desired amount of trapped Exhaust Gas Recirculation (EGR) from the previous cycle, the phase of the Intake Valves Opening (IVO) and Exhaust Valves Closing (EVC) are designed to vary in this model. The model is able to anticipate the auto-ignition timing, MFB, combustion duration, in-cylinder pressure and hydrocarbon emissions etc. The validation has been conducted by comparing experimental data obtained from a Jaguar HCCI/SI V6 GDI engine at the University of Birmingham. This comparison shows good agreement between the simulation results and experimental data. Copyright © 2012 SAE International.

Thermal models of buildings are often used to identify energy savings within a building. Given that a significant proportion of that energy is typically used to maintain building temperature, establishing the optimal control of the buildings thermal system is important. This requires an understanding of the thermal dynamics of the building, which is often obtained from physical thermal models. However, these models require detailed building parameters to be specified and these can often be difficult to determine. In this paper, we propose an evolutionary approach to parameter identification for thermal models that are formulated as an optimization task. A state-of-the-art evolutionary algorithm, i.e., SaNSDE+, has been developed. A fitness function is defined, which quantifies the difference between the energy-consumption time-series data that are derived from the identified parameters and that given by simulation with a set of predetermined target model parameters. In comparison with a conventional genetic algorithm, fast evolutionary programming, and two state-of-the-art evolutionary algorithms, our experimental results show that the proposed SaNSDE+ has significantly improved both the solution quality and the convergence speed, suggesting this is an effective tool for parameter identification for simulated building thermal models.

The rise of social network usage through mobile devices makes the rigorous analysis of these systems of paramount importance for intelligence gathering and decision making. This paper describes the outcome of a multidisciplinary project carried out for the purpose of Twitter data collection and analysis. In particular, a proposal for enabling the discovery of spatial patterns within geo-located Twitter content has been investigated and implemented.

In this paper, we propose an intelligent data-analysis method for modeling and prediction of daily electricity consumption in buildings. The objective is to enable a building-management system to be used for forecasting and detection of abnormal energy use. First, an outlier-detection method is proposed to identify abnormally high or low energy use in a building. Then a canonical variate analysis is employed to describe latent variables of daily electricity-consumption profiles, which can be used to group the data sets into different clusters. Finally, a simple classifier is used to predict the daily electricity-consumption profiles. A case study, based on a mixed-use environment, was studied. The results demonstrate that the method proposed in this paper can be used in conjunction with a building-management system to identify abnormal utility consumption and notify building operators in real time.

Evolutionary Art is the application of artificial evolution to the creation of artworks. Most evolutionary art implementations share one characteristic: being locked into a fixed, highly distinguishable 'style' (the 'signature problem'). While the cause is readily identified as the representation used, the reason lies deeper: since selection is performed by (slow) humans, systems have to perform with extremely little fitness information. We suggest two approaches to overcome this problem. On the one hand, we can use computer support to remove the evaluation bottleneck. On the other hand, we can make it easier to change design spaces. To evaluate these ideas, we propose a realworld project supporting designer-user collaboration in mass customization.

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Target shape matching can be used as a quick and easy surrogate task when evaluating optimization algorithms intended for computationally expensive tasks, such as turbine blade design using computational fluid dynamics. Many reasonable shape representations render the shape matching fitness landscape linearly separable, unlike that of the turbine design task. Optimization algorithms may exploit this property, so evaluations based on shape matching may be inappropriate for turbine design. To address this disparity, a method is proposed to generate a linearly non-separable shape matching fitness landscape: the test shape is compared to one or more distracter shapes as well as the target, and the individual fitness measures are combined non-linearly. A demonstration is given, using a simple polygon representation and exhaustively varying two parameters, to show that this method does indeed generate a fitness landscape that cannot be linearly decomposed.

Target shape design optimization problem (TS-DOP) is a miniature model for real world design optimization problems. It is proposed as a test bed to design and analyze optimization approaches for design optimization with tremendously reducing the running period of optimization process, while, the merit can be only achieved by correctly approximating the real design situation and satisfying the causality of design and evaluation. The representation of the designed object is mostly described by parameterization techniques. To realize the design optimization, is to vary the parameterized object by means of operating the relevant parameters. The solution of design optimization often involved the choice of suitable description for the designed object, which can be obtained by expanding the design freedom. When changing the description length, the original parameters of the designed object will then varied. This bring about the requirements for optimization algorithms to self-adapt their strategy parameters and related variables to perform consistently searching. We first put forwards a revised fitness evaluation mechanism for the TSDOP in order to more reasonably check the designed shape and direct optimization procedures. Based on the revised TSDOP framework, we further discuss the parameter setting problem for algorithms, especially evolution strategies, to adapt and initial their search strategy parameters. A solution method is proposed with solving a linear equations by a recursive way with linear time complexity. All discussions are limited with the B-spline parameterization framework, but may generally suit other parameterization techniques. Experiments are used to verify the causality of the revised fitness evaluation mechanism and to study the significance of the proposed method for suitable parameter settings of optimization algorithms during the adaptation of the description length for design optimization.

Learning genetic representation has been shown to be a useful tool in evolutionary computation. It can reduce the time required to find solutions and it allows the search process to be biased towards more desirable solutions. Learning genetic representation involves the bottom-up creation of evolved genes from either original (basic) genes or from other evolved genes and the introduction of those into the population. The evolved genes effectively protect combinations of genes that have been found useful from being disturbed by the genetic operations (cross-over, mutation). However, this protection can rapidly lead to situations where evolved genes interlock in such a way that few or no genetic operations are possible on some genotypes. To prevent the interlocking previous implementations only allow the creation of evolved genes from genes that are direct neighbours on the genotype and therefore form continuous blocks. In this paper it is shown that the notion of dominant and recessive genes can be used to remove this limitation. Using more than one gene at a single location makes it possible to construct genetic operations that can separate interlocking evolved genes. This allows the use of non-continuous evolved genes with only minimal violations of the protection of evolved genes from those operations. As an example, this paper shows how evolved genes with dominant and recessive genes can be used to learn features from a set of Mondrian paintings. The representation can then be used to create new designs that contain features of the examples. The Mondrian paintings can be coded as a tree, where every node represents a rectangle division, with values for direction, position, linewidth and colour. The modified evolutionary operations allow the system to create non-continuous evolved genes, for example associate two divisions with thin lines, without specifying other values. Analysis of the behaviour of the system shows that about one in ten genes is a dominant/recessive gene pair. This shows that while dominant and recessive genes are important to allow the use of noncontinuous evolved genes, they do not occur often enough to seriously violate the protection of evolved genes from genetic operations.

In this paper, we show how artificial evolution can be used to improve the fault-tolerance of electronic circuits. We show that evolution is able to improve the fault tolerance of a digital circuit, given a known fault model. Evolution is also able to create sets of different circuits that, when combined into an ensemble of circuits, have reduced correlation in their fault pattern, and therefore improved fault tolerance. An important part of the algorithm used to create the circuits is a measure of the correlation between the fault patterns of different circuits. Using this measure in the fitness, the circuits evolve towards different, highly fault-tolerant circuits. The measure also proves very useful for fitness sharing purposes. We have evolved a number of circuits for a simple 2 x 3 multiplier problem, and use these to demonstrate the performance under different simulated fault models.

Evolutionary approaches have been used in a large variety of design domains, from aircraft engineering to the designs of analog filters. Many of these approaches use measures to improve the variety of solutions in the population. One such measure is clustering. In this paper, clustering and Pareto optimisation are combined into a single evolutionary design algorithm. The population is split into a number of clusters, and parent and offspring selection, as well as fitness calculation, are performed on a per-cluster basis. The objective of this is to prevent the system from converging prematurely to a local minimum and to encourage a number of different designs that fulfil the design criteria. Our approach is demonstrated in the domain of digital filter design. Using a polar coordinate based pole-zero representation, two different lowpass filter design problems are explored. The results are compared to designs created by a human expert. They demonstrate that the evolutionary process is able to create designs that are competitive with those created using a conventional design process by a human expert. They also demonstrate that each evolutionary run can produce a number of different designs with similar fitness values, but very different characteristics.

Evolutionary methods are nowb eginning to be used routinely in design applications. However, even with computing speeds growing continuously, for many complex design problems evolutionary computing times are so long that their use is not practical. Divide and conquer based methods sometimes improve the situation, but in most cases the biggest speed improvement can be gained by adding domain knowledge. Combining evolutionary methods with conventional design methods is one way of doing this. This paper shows how evolutionary computation can be used to improve designs created by conventional design methods. A digital filter design problem is used to illustrate howa conventionally derived design can be further improved by evolutionary calibration. Our experimental results showthat the evolutionary calibration algorithm is able to consistently improve the original designs by a considerable margin.

Although evolutionary algorithms are very different from other artificial intelligence search algorithms, they face similar fundamental issues-representation and searching. There has been a large amount of work done in evolutionary computation on searching, such as recombination operators, mutation operators, selection schemes and various specialised operators. In comparison, research on different representations has not been as active. Most such research has been focused on a single representation, e.g. bit strings, real-valued vectors using Cartesian coordinates, etc. This paper proposes and studies multiple representations in an evolutionary algorithm and shows empirically how multiple representations can benefit searches as much as a good search operator could.

If a computer is to create designs with the goal of following a certain style it has to have information about this style. Unfortunately, the most often used method of formal representations of style, shape grammars, does not lend itself to automated implementation. However, It has been shown how an evolutionary system with evolving representation can provide an alternative approach that allows a system to learn style knowledge automatically and without the need for an explicit representation. This paper shows how the applicability of evolved representation can be extended by the introduction of transformations of the representation. One such transformation allows mixing of style knowledge, similar to the cross-breeding of animals of different races, with the added possibility of controlling exactly what features are used from which source. This can be achieved through different ways of mixing representations learned from different examples and then using the new, combined representation to create new designs. In a similar manner, information learned in one application domain can be used in a different domain. To achieve this, either the representation or the genotype-phenotype transformation has to be adapted. The same operations also allow mixing of knowledge from different domains. As an example, we show how style information learned from a set of Mondrian paintings can be combined with style information from a Frank Lloyd Wright window design, to create new window designs. Also, we show how the combined style information can then be used to create three-dimensional objects, showing style features similar to the newly designed windows.

ii1 Introduction 11.1 Goal of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 22 Background 42.1 Evolutionary Algorithms Background . . . . . . . . . . . . . . . . . . 42.1.1 Variable-length Representation . . . . . . . . . . . . . . . . . . 62.1.2 Genotype-Phenotype Distinction . . . . . . . . . . . . . . . . . 62.1.3 Multi-level Representation of Designs . . . . . . . . . . . . . ....