High-level analog circuit design is a complex problem domain in which evolutionary search has recently produced encouraging results. However, little is known about how to best structure evolution for these tasks. The choices of circuit representation, fitness evaluation technique, and genetic operators clearly have a profound effect on the search process. In this paper, we examine fitness evaluation by comparing the effectiveness of four fitness schedules. Three fitness schedules are dynamic – the evaluation function changes over the course of the run, and one is static. Coevolutionary search is included, and we present a method of evaluating the problem population that is conducive to multiobjective optimization. Twenty-five runs of an analog amplifier design task using each fitness schedule are presented. The results indicate that solution quality is highest with static and coevolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency. 1

Evolvable Hardware (EHW) has been proposed as a new method for designing systems for real-world applications. This paper contains a classification of the published work on this topic. Further, a thorough discussion about the limitations of the present EHW and possible solutions to these are proposed. EHW has been applied to a wide range of applications. However, to solve more complex applications, the evolutionary schemes should be improved.

To realize design automation of dynamic systems, there are two major issues to be dealt with: open-topology generation of dynamic systems and simulation or analysis of those models. For the first issue, we exploit the strong topology exploration capability of genetic programming to create and evolve structures representing dynamic systems. With the help of ERCs (ephemeral random constants) in genetic programming, we can also evolve the sizing of dynamic system components along with the structures. The second issue, simulation and analysis of those system models, is made more complex when they represent mixed-energydomain systems. We take advantage of bond graphs as a tool for multi- or mixed-domain modeling and simulation of dynamic systems. Because there are many considerations in dynamic system design that are not completely captured by a bond graph, we would like to generate multiple solutions, allowing the designer more latitude in choosing a model to implement. The approach in this paper is capable of providing a variety of design choices to the designer for further analysis, comparison and trade-off. The approach is shown to be efficient and effective in an example of open-ended realworld dynamic system design application, a printer re-design problem. 1

This paper introduces an automated circuit design system for the evolution and subsequent invention of CMOS amplifiers. The proposed system relies on a mix of genetic programming and a new topologyindependent design optimisation method referred to as current-flow analysis. Genetic programming evolves new circuit topologies from the collection of primitive devices and basic building blocks. Current-flow analysis screens and corrects circuits using topology-independent design rules. Experimental results show a promising improvement on the design of operational amplifiers that making the automated analogue design environment using genetic programming a lot more practical. I.

Abstract. Evolvable Hardware (EHW) is a scheme- inspired by natural evolution, for automatic design of hardware systems. By exploring a large design search space, EHW may find solutions for a task, unsolvable, or more optimal than those found using traditional design methods. During evolution it is necessary to evaluate a large number of different circuits which is normally most efficiently undertaken in reconfigurable hardware. For digital design, FPGAs (Field Programmable Gate Arrays) are very applicable. Thus, this technology is applied in much of the work with evolvable hardware. The paper introduces EHW and outlines how it can be applied for hardware design of real-world applications. It continues by discussing the main problems and possible solutions. This includes improving the scalability of evolved systems. Promising features of EHW will be addressed as well, including run-time adaptable systems. 1

This dissertation concerns the principles and techniques for scalable evolutionary computation to achieve better solutions for larger problems with more computational resources. It suggests that many of the limitations of existent evolutionary algorithms, such as premature convergence, stagnation, loss of diversity, lack of reliability and efficiency, are derived from the fundamental convergent evolution model, the oversimplified “survival of the fittest” Darwinian evolution model. Within this model, the higher the fitness the population achieves, the more the search capability is lost. This is also the case for many other conventional search techniques. The main result of this dissertation is the introduction of a novel sustainable evolution model, the Hierarchical Fair Competition (HFC) model, and corresponding five sustainable evolutionary algorithms (EA) for evolutionary search. By maintaining individuals in hierarchically organized fitness levels and keeping evolution going at all fitness levels, HFC transforms the conventional convergent evolutionary computation model into a sustainable search framework by ensuring a continuous supply and incorporation of low-level building blocks and by culturing and maintaining building blocks of intermediate levels with its

Abstract—Product design engineering is undergoing a transformation from informal and largely experience-based discipline to a science-based domain. Computational intelligence (CI) offers models and algorithms that can contribute greatly to design formalization and automation. This paper surveys CI concepts and approaches applicable to product design engineering. Taxonomy of the surveyed literature is presented according to the generally recognized areas in both product design engineering and CI. Some research issues that arise from the broad perspective presented in the paper have been signaled but not fully pursued. No survey of such a broad field can be complete; however, the material presented in the paper is a summary of state-of-the-art CI concepts and approaches in product design engineering. Index Terms—Computational intelligence (CI), decision making, design automation, engineering design, product engineering. I.

Abstract. This paper introduces a new asynchronous parallel evolutionary algorithm (APEA) based on the island model for solving function optimization problems. Our fully distributed APEA overlaps the communication and computation efficiently and is inherently fault-tolerant in a large-scale distributed computing environment. For the scalable BUMP problem, our APEA algorithm achieves the best solution for the 50-dimension problem, and is the first algorithm of which we are aware that can solve the 1,000,000dimension problem. For other benchmark problems, our APEA finds the best solution to G7 in fewer time steps than [16,17], and finds a better solution to G10 than [17]. 1

In general, design of mechatronic systems includes two steps: conceptual design and detailed design. In the conceptual design phase, the following questions should be answered (Tay et al., 1998): 1) what is the exact design problem to be solved? (This

Most existing research on robust design using evolutionary algorithms (EA) follows the paradigm of traditional robust design, in which parameters of a design solution are tuned to improve the robustness of the system. However, the topological structure of a system may set a limit on the possible robustness achievable through parameter tuning. This paper proposes a new robust design paradigm that exploits the open-ended topological synthesis capability of genetic programming to evolve more robust systems. As a case study, a methodology for automated synthesis of dynamic systems, based on genetic programming and bond graph modeling (GPBG), is applied to evolve robust low-pass and high-pass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), it is shown that open-ended topology search by genetic programming with a fitness criterion rewarding robustness can evolve more robust systems with respect to parameter perturbations than what was achieved through parameter tuning alone, for our test problems.