Design is fundamental to software development but can be demanding to perform. Thus, to assist the software designer, evolutionary computing is being increasingly applied using machine-based, quantitative fitness functions to evolve software designs. However, in nature, elegance and symmetry play a crucial role in the reproductive fitness of various organisms. In addition, subjective evaluation has also been exploited in interactive evolutionary computation (IEC). Therefore, to investigate the role of elegance and symmetry in software design, four novel elegance measures are proposed which are based on the evenness of distribution of design elements. In controlled experiments in a dynamic IEC environment, designers are presented with visualizations of object-oriented software designs, which they rank according to a subjective assessment of elegance. For three out of the four elegance measures proposed, it is found that a significant correlation exists between elegance values and reward elicited. These three elegance measures assess the evenness of distribution of 1) attributes and methods among classes; 2) external couples between classes; and 3) the ratio of attributes to methods. It is concluded that symmetrical elegance is in some way significant in software design, and that this can be exploited in dynamic, multiobjective IEC to produce elegant software designs.

User-centered Interactive Evolutionary Computation (IEC) has been applied to a wide variety of areas, including UML software design. The performance of evolutionary search is important as user interaction fatigue remains an on-going challenge in IEC. However, to obtain optimal search performance, it is usually necessary to “tune” evolutionary control parameters manually, although tuning control parameters can be time-consuming and error-prone. To address this issue in other fields of evolutionary computation, dynamic parameter control including deterministic, adaptive and self-adaptive mechanisms have been applied extensively to real-valued representations. This paper postulates that dynamic parameter control may be highly beneficial to IEC in general, and UML software design in particular, wherein a novel object-based solution representation is used. Three software design problems from differing design domains and of differing scale have been investigated with mutation probabilities modified by simulated annealing, the Rechenberg “1/5 success rule” and self-adaptation within local search. Results indicate that self-adaptation appears to be the most robust and scalable mutation probability modification mechanism. The use of self-adaption with an object-based representation is novel, and results indicate that dynamic parameter control offers great potential within IEC.

Although much evidence exists to suggest that early life cycle software engineering design is a difficult task for software engineers to perform, current computational tool support for software engineers is limited. To address this limitation, interactive search-based approaches using evolutionary computation and software agents are investigated in experimental upstream design episodes for two example design domains. Results show that interactive evolutionary search, supported by software agents, appears highly promising. As an open system, search is steered jointly by designer preferences and software agents. Directly traceable to the design problem domain, a mass of useful and interesting class designs is arrived at which may be visualized by the designer with quantitative measures of structural integrity, such as design coupling and class cohesion. The class designs are found to be of equivalent or better coupling and cohesion when compared to a manual class design for the example design domains, and by exploiting concurrent execution, the runtime performance of the software agents is highly favorable.

Conceptual software engineering design is an intensely people-oriented and non-trivial activity, yet current computational tool support is limited. While a number of search-based software engineering approaches to support software design have been reported, few empirical studies into their application have been described. This paper reports the findings of an observational study of conceptual design episodes in a UK higher education problem domain. When compared with a manual design episode, a design episode enabled by a user-interactive, search-based, evolutionary computation tool generates a large number of useful and interesting candidate designs, and provides enhanced qualitative and quantitative evaluation. It is also found that tool-supported visualization of UML class designs offers opportunities for sudden design discovery, and that designers respond positively to opportunities to explore and exploit multiple candidate designs. It appears therefore that search-based computational tool support offers high potential in the support of conceptual software engineering design.

Although much evidence exists to suggest that conceptual software engineering design is a difficult task for software engineers to perform, current computationally intelligent tool support for software engineers is limited. While search-based approaches involving module clustering and refactoring have been proposed and show promise, such approaches are downstream in terms of the software development lifecycle - the designer must manually produce a design before search-based clustering and refactoring can take place. Interactive, user-centered search-based approaches, on the other hand, support the designer at the beginning of, and during, conceptual software design, and are investigated in this paper by means of a case study. Results show that interactive evolutionary search, supported by software agents, appears highly promising. As an open system, search is steered jointly by designer preferences and software agents. Directly traceable to the design problem domain, a mass of useful and interesting conceptual class designs are arrived at which may be visualized by the designer with quantitative measures of structural integrity such as design coupling and class cohesion. The conceptual class designs are found to be of equivalent or better coupling and cohesion when compared to a manual conceptual design of the case study, and by exploiting concurrent execution, the performance of the software agents is highly favorable.

While recent attempts to search a conceptual software engineering design search space with multi-objective evolutionary algorithms have yielded promising results, the practical application of such search-based techniques remains to be addressed. This paper reports initial findings of the application of software agents in support of an interactive, user-centered conceptual software design scenario. The supporting role of a number of single responsibility agents is described and results for a case study indicate that the application of such agents to search-based design scenarios provides efficient, high performance and effective support. The notion of interactive, joint human-computer activity appears to map well to conceptual software design scenarios: focus on superior design concepts and thence to useful and interesting designs provides a natural and effective way of narrowing the population based search. In addition, agents and the human designer appear to interact as cooperative "team players", jointly influencing the evolutionary algorithm based search. Nevertheless, challenges remain, including expanding the scale of implementation of underlying technologies to support distributed, collaborative design.

The paper describes further research relating to an interactive evolutionary design system (IEDS) with emphasis upon extending it to support free-form design. A generative feedback loop is also introduced which, when using a component based representation, extracts user preferences regarding the various components at run-time. This information is fed back into the system to enhance the quality of the generated solutions. The background work leading to the free- form version of the IEDS for urban furniture design is introduced followed by a description of the component preferences extracted from user evaluated solutions. Finally, results of a multi-user comparative study between randomly generated solutions and solutions generated using preference data is presented.

A number of evolutionary based techniques have been investigated in the field of in silico drug discovery. This paper contrasts previous work carried out within Evotec and the ACDDM Lab in the area of chemical library design with techniques currently under investigation for de novo molecule design. These drug design spaces are highly complex, and present many difficulties applying standard search and optimisation techniques. The problems dealing with these search spaces, and the application of computational techniques have been described along with reasons for selecting the most appropriate techniques mirroring the user processes in the laboratory.

This research is a continuation of previous work by the authors relating to the inclusion of aesthetic criteria within an interactive evolutionary design system. The work described extends the design system re the manipulation of a more complex design problem with increased importance placed on aesthetic criterion. The paper initially introduces the previous work before describing more recent research and positioning this in terms of previously published work. Finally, initial results from the further developed design system are presented.

This poster paper investigates the potential of single and multi-objective genetic operators with an object-oriented conceptual design space. Using cohesion as an objective fitness function, genetic operators inspired by genetic algorithms and evolutionary programming are compared against a simple case study. Also, using both cohesion and coupling as objective fitness functions, multi-objective genetic operators inspired by a non-dominated sorting algorithm have been developed. Cohesion and coupling values achieved are similar to human performed designs and a large number and variety of optimal solutions are arrived at, which could not have been produced by the human software engineer. We conclude that this mass of optimal design variants offers significant potential for design support when integrated with user-centric, computationally intelligent tools.

In silico combinatorial library design involves the identification of molecules that have a greater probability of exhibiting desired biological activity when subjected to in vitro screening (assaying) against a particular biological target. The paper introduces the integration of cluster-oriented genetic algorithms (COGAs) with such machine-based library design environments. COGAs have a proven capability to identify high-performance regions of complex, continuous design spaces relating to engineering design problems. Modifications to the basic COCA approach are described that allow a transfer of this capability from continuous variable parameter space to the highly discrete spaces described by reactants across reagent libraries. Results relating firstly to the identification of optimal molecules and secondly to the focussing of reagent libraries in terms of high-performance reactants are presented. Single objective optimisation and focussing are initially considered before moving on to multiple objective satisfaction

The paper speculates upon the development of human-centric evolutionary conceptual design systems that support implicit learning through the succinct visual presentation of data relating to both variable and objective space. Various perspectives of multi-objective design information support a constantly improving understanding of both subjective and quantitative relationships between variables and objectives. This information emerges from cluster-oriented genetic algorithm (COGA) output and is further defined by appropriate data mining, processing and visualization techniques. The intention is to support implicit learning and reduce complexity through the presentation of differing perspectives relating to solution / objective interaction and dependencies. It is proposed that the developing systems could support intuitional understanding of the problem domain. Further proposed agent-based support and interactive elements for the various processes are also introduced.

The paper describes a new preference method and its use in multiobjective optimization. These preferences are developed with a goal to reduce the cognitive overload associated with the relative importance of a certain criterion within a multiobjective design environment involving large numbers of objectives. Their successful integration with several genetic-algorithm-based design search and optimization techniques (weighted sums, weighted Pareto, weighted co-evolutionary methods, and weighted scenarios) are described and theoretical results relating to complexity and sensitivity of the algorithm are presented and discussed. Its usefulness was demonstrated in a real-world project of conceptual airframe design.

Simple statistical techniques are investigated to support Cluster-Oriented Genetic Algorithm processes. In a real world application this analysis improves efficiency by reducing effort relating to exploration of multiple visual representations. The preliminary results suggest that the designer can easily be guided rewards interesting areas of high performance regions by viewing the most appropriate two- and three-dimensional plots.

The paper introduces the concept of an Interactive Evolutionary Design System (IEDS) that supports the engineering designer during the conceptual/preliminary stages of the design process. Requirement during these early stages relates primarily to design search and exploration across a poorly defined space as the designer's knowledge base concerning the problem area develops. Multiobjective satisfaction plays a major role, and objectives are likely to be ill-defined and their relative importance uncertain. Interactive evolutionary search and exploration provides information to the design team that contributes directly to their overall understanding of the problem domain in terms of relevant objectives, constraints, and variable ranges. This paper describes the development of certain elements within an interactive evolutionary conceptual design environment that allows off-line processing of such information leading to a redefinition of the design space. Such redefinition may refer to the inclusion or removal of objectives, changes concerning their relative importance, or the reduction of variable ranges as a better understanding of objective sensitivity is established. The emphasis, therefore, moves from a multiobjective optimization over a preset number of generations to a relatively continuous interactive evolutionary search that results in the optimal definition of both the variable and objective space relating to the design problem at hand. The paper describes those elements of the IEDS relating to such multiobjective information gathering and subsequent design space redefinition.

It is during conceptual design that neither the problem space nor solution space is fixed and the designers aim is an exploration of a range of possible problem-solution scenarios. Within such a framework the constraints placed upon the initial limits assigned to the design variables are subject to change. Thus this paper presents a multi-population GA approach that allows the search to move outside of its pre-defined limits. Utilising dynamic fitness sharing and directional search techniques sub-populations are encouraged to explore differing high performance and unspecified areas of the search space.

This paper presents an empirical investigation of the integration of the adaptive filter (developed for use with COGAs) with a series of evolutionary search algorithms. The relative merits of each technique are compared with variable mutation COGA (vmCOGA) (Parmee, 1996) using a suite of two-dimensional test functions and a series of performance measures. The generic capabilities of COGA are demonstrated by illustrating its ability to rapidly decompose multidimensional search spaces described by continuous and discrete parameter, real-world design models into regions of high performance. These models relate to conceptual airframe design and compressor blade cooling within a gas turbine engine. This investigation further supports the use of the COGA strategy as a conceptual, engineering design support tool.

In this paper we present a genetic algorithm based system for conceptual engineering design. First, we present a method based on preference relations for transforming non-crisp (qualitative) relationships between objectives in multi-objective optimisation into quantitative attributes (numbers). This is integrated with two multiobjective genetic algorithms: weighted sums GA and a method for combining the Pareto method with weights. Examples of preference relations application together with traditional genetic algorithms are also presented. A further method for dynamical inclusion and modification of extra constraints (not included in the mathematical model of the system) via scenarios is presented. Its use is discussed and potential applications indicated. Finally, some future work paths are mentioned.

The paper discusses the requirements of the engineering designer during the higher levels of the design process in terms of search within initial predefined design spaces and subsequent exploration of transformed design spaces. Such transformation is based upon information gathered from initial search using evolutionary techniques that rapidly identify high-performance regions of complex design spaces. It is proposed that off-line processing of such information and associated design team discussion in an iterative designer/evolutionary search procedure can result in significant changes in the design environment that will lead to the human/machine based discovery of innovative design solutions. Research relating to appropriate evolutionary search and exploratory procedures is described and illustrated with results from simple test functions and real-world design problems. These results support discussion related to the manner in which the techniques can best support innovative design activity in an interactive design team/evolutionary search environment.