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Mammography is the keystone of breast cancer screening. Yet high sensitivity is achieved at the cost of low specificity - only one-third of patients recalled will have breast cancer. Computer-aided detection (CAD) is a potentially valuable tool for assisting the breast radiologist to improve positive prediction values. However, to date, CAD has not reliably altered screening outcomes and the large proportion of false positives remains a drawback. We describe a novel method to improve CAD performance called Cartesian Genetic Programming (CGP); a machine-based learning algorithm, akin to genetic evolution.

cote interne IRCAM: Stepney12a; National audience; Today’s artefacts, from small devices to buildings and cities, are, or are becoming, cyber-physical socio-technical systems, with tightly interwoven material and computational parts. Currently, we have to la- boriously build such systems, component by component, and the results are often difficult to maintain, adapt, and reconfigure. Even “soft”ware is brittle and non-trivial to adapt and change. If we look to nature, how- ever, large complex organisms grow, adapt to their environment, and repair themselves when damaged. In this position paper, we present Gro-CyPhy, an unconventional computational framework for growing cyber-physical systems from com- putational seeds, and gardening the growing systems, in order to adapt them to specific needs. The Gro-CyPhy architecture comprises: a Seed Factory, a process for designing specific computational seeds to meet cyber-physical system requirements; a Growth Engine, providing the computational processes that grow seeds in simulation; and a Computational Garden, where mul- tiple seeds can be planted and grown in concert, and where a high-level gardener can shape them into complex cyber-physical systems. We outline how the Gro-CyPhy architecture might be applied to a significant exemplar application: a (simulated) skyscraper, comprising several mutually interdependent physical and virtual subsystems, such as the shell of exterior and interior walls, electrical power and data net- works, plumbing and rain-water harvesting, heating and air-conditioning systems, and building management control systems.

A fast learning neuro-evolutionary technique that evolves Artificial Neural Networks using Cartesian Genetic Programming (CGPANN) is used to detect the presence of breast cancer. Features from breast mass are extracted using fine needle aspiration (FNA) and are applied to the CGPANN for diagnosis of breast cancer. FNA data is obtained from the Wisconsin Diagnostic Breast Cancer website and is used for training and testing the network. The developed system produces fast and accurate results when compared to contemporary work done in the field. The error of the model comes out to be as low as 1% for Type-I (classifying benign sample falsely as malignant) and 0.5% for Type-II (classifying malignant sample falsely as benign).

Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program.Since then, the classical form of CGP has been developed made more efficient in various ways. Notably, by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on).The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.

A new method is presented for calculating fixed polarity Reed-Muller expansions from the boolean minterms. Direct transformation equations of Reed-Muller expansions with polarity are derived. A highly efficient and flexible exhaustive search algorithm is presented which can obtain an optimum polarity more quickly if a sub-optimum polarity is obtained first. Also exact formulae are presented for optimum polarities for any three-variable logic function.

Two algorithms are presented, the first is a technique to determine good, though not necessarily optimum, fixed polarity Reed-Muller expansions of completely specified boolean functions. The second algorithm determines the allocations of the ‘don't care’ terms of incompletely specified boolean functions resulting in optimum positive polarity Reed-Muller expansions. Additionally, investigations are made into combining these two techniques to determine fixed polarity Reed-Muller expansions of incompletely specified boolean functions. Results are presented which show the effectiveness of the techniques and comparisons are made with existing methods.

Self-Modifying Cartesian Genetic Programming (SMCGP) is a general purpose, graph-based, developmental form of Cartesian Genetic Programming. In addition to the usual computational functions found in CGP, SMCGP includes functions that can modify the evolved program at run time. This means that programs can be iterated to produce an infinite sequence of phenotypes from a single evolved genotype. Here, we discuss the results of using SMCGP on a variety of different problems, and see that SMCGP is able to solve tasks that require scalability and plasticity. We demonstrate how SMCGP is able to produce results that would be impossible for conventional, static Genetic Programming techniques.

In nature, modules pervade organisms at all levels and construct complex and scalable organisms. Modularity is fundamental for achieving large and complex systems. In engineering design, as required systems become larger, the complex and modularity of the design becomes more important. Inspired by nature, this paper introduces a bottom-up construction method to construct digital circuits using gene regulation in a cellular system. It uses modular design to achieve scalable combinatorial systems.

Self-Modifying Cartesian Genetic Programming (SMCGP) is a form of genetic programming that integrates developmental (self-modifying) features as a genotype-phenotype mapping. This paper asks: Is it possible to evolve a learning algorithm using SMCGP?

The design of electronic circuits with model-free heuristics like evolutionary algorithms is an attractive concept and field of research. Although successful to a point, evolution of circuits that are bigger than a 3-bit multiplier is hindered by the scalability problem. Modelling the biological development as an artificial genotype-phenotype mapping mechanism has been shown to improve scalability on some simple circuit problems and pattern formations. As a candidate solution to the scalability issue, an artificial developmental system is presented.

Real working agents normally operate in dynamic changing environments. These changes could either affect the efficiency of the agents' performance or even damage the functionality of the agent. Robustness is the key requirement to solve this problem. Inspired by nature, this paper demonstrates two mechanisms that contribute to individual's robustness in changing environments: evolution and degeneracy. Through evolution in damaging environment, evolved agents have to cope with changes in the environment and acquire robustness. Through degeneracy, individuals can maintain their fitness even when some damaged parts are involved in system function.

A model for intrinsic artificial development is introduced in this paper. The proposed model features a novel mechanism where growth emerges, rather than being triggered by a single action. Different types of cell signalling ensure that breaking symmetries is rather the norm than an exception, and gene activity is regulated on two layers: first, by the proteins that are produced by the gene regulatory network (GRN). Second, through structural feedback by second messenger molecules, which are not directly produced through gene expression, but are produced by sensor proteins, which take the cell's structure into account. The latter feedback mechanism is a novel approach, intended to enable adaptivity and environment coupling in real-world applications. The model is implemented in hardware, and is designed to run autonomously in resource limited embedded systems. Initial experiments are carried out to measure long-term stability, dynamics, adaptivity and scalability of the new approach. Furthermore the ability of the GRN to produce patterns of different symmetries is examined.

Many researchers have encountered the problem that the evolution of electronic circuits becomes exponentially more difficult when problems with an increasing number of outputs are tackled. Although this is an issue in both intrinsic and extrinsic evolution experiments, overcoming this problem is particularly challenging in the case of evolvable hardware, where logic and routing resources are constrained according to the given architecture. Consequently, the success of experiments also depends on how the inputs and outputs are interfaced to the evolvable hardware. Various approaches have been made to solve the multiple output problem: partitioning the task with respect to the input or output space, incremental evolution of sub-tasks or resource allocation. However, in most cases, the proposed methods can only be applied in the case of extrinsic evolution. In this paper, we have accordingly, focused on scaling problem of increasing numbers of outputs when logic circuits are intrinsically evolved. We raise a number of questions: how big is the performance drop when increasing the number of outputs? Can the resources of evolvable hardware be structured in a suitable way to overcome the complexity imposed by multiple outputs, without including knowledge about the problem domain? Can available resources in hardware still be efficiently used when pre-structured? In order to answer these questions, different structural implementations are investigated. We have looked at these issues in solving three problems: 4-bit parity, 2-bit adder and 2-bit multiplier.

Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It differs from CGP by including primitive functions which modify the program. Beginning with the evolved genotype the self-modifying functions produce a new program (phenotype) at each iteration. In this paper we have applied it to a well known digital circuit building problem: even-parity. We show that it is easier to solve difficult parity problems with SMCGP than either with CGP or Modular CGP, and that the increase in efficiency grows with problem size. More importantly, we prove that SMCGP can evolve general solutions to arbitrary-sized even parity problems.

Biological neurons are extremely complex cells whose morphology grows and changes in response to the external environment. Yet, artificial neural networks (ANNs) have represented neurons as simple computational devices. It has been evident for a long time that ANNs have learning abilities that are insignificant compared with some of the simplest biological brains. We argue that we understand enough neuroscience to create much more sophisticated models. In this paper, we report on our attempts to do this.We identify and evolve seven programs that together represents a neuron which grows post evolution into a complete 'neurological' system. The network that occurs by running the programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change. We have evaluated the capability of these networks for playing the game of checkers. Our method has no board evaluation function, no explicit learning rules and no human expertise at playing checkers is used. The learning abilities of these networks are encoded at a genetic level rather than at the phenotype level of neural connections.

The design of a new biologically inspired artificial developmental system is described in this paper. In general, developmental systems converge slower and are more computationally expensive than direct evolution. However, the performance trends of development indicate that the full benefit of development will arise with larger and more complex problems that exhibit some sort of regularity in their structure: thus, the aim is to evolve larger electronic systems through the modularity allowed by development. The hope is that the proposed artificial developmental system will exhibit adaptivity and fault tolerance in the future. The cell signalling and the system of Gene Regulatory Networks present in biological organisms are modelled in our developmental system, and tailored for tackling real world problems on electronic hardware. For the first time, a Gene Regulatory Network system is successfully shown to develop the complete circuit structure of a desired digital circuit without the help of another mechanism or any problem specific structuring. Experiments are presented that show the modular behaviour of the developmental system, as well as its ability to solve non-modular circuit problems.

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that usin...

Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It is able to modify its own phenotype during execution of the evolved program. This is done by the inclusion of modification operators in the function set. Here we present the use of the technique on several different sequence generation and regression problems.

We present a method for constructing electronic circuits that uses analogues of biological multi-cellular development, genetic regulatory networks, and transcription and translation processes to build circuits. We show how small circuits may be evolved and how they may be reused to build larger circuits. We also demonstrate that the artificial `organisms' are capable of regeneration so that circuit functionality can be recovered after damage.

This paper presents a method for co-evolving neuro-inspired developmental programs for playing checkers. Each player's program is represented by seven chromosomes encoding digital circuits, using a form of genetic programming, called Cartesian Genetic Programming (CGP). The neural network that occurs by running the genetic programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change in response to situations encountered on the checkers board. The results show that, after a number of generations, by playing each other the agents play much better than those from earlier generations. Such learning abilities are encoded at a geneticlevel rather than at the phenotype level of neural connections.

Evolution is particularly good at finding specific solutions, which are only valid for exactly the input and environment that are presented during evolution. In most evolution experiments the input pattern order problemis not considered, even though the ability to provide a correct result for any input pattern is a prerequisite for valid circuits. Therefore, the importance of including randomness in the input pattern applied during evolution is addressed in this paper. This is shown to be mandatory--particularly in the case of unconstrained intrinsic evolution of digital circuits--in order to find valid solutions. The different ways in which unconstrained evolution and constrained evolution exploit resources of a hardware substrate are compared. It is also shown that evolution benefits from versatile input configurations. Furthermore, hierarchical fitness functions, previously introduced to improve the evolution of combinatorial circuits, are applied to the evolution of sequential circuits.

A developmental model of neural network is presented and evaluated in the game of Checkers. The network is developed using cartesian genetic programs (CGP) as genotypes. Two agents are provided with this network and allowed to co-evolve untill they start playing better. The network that occurs by running theses genetic programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change in response to situations encountered on the checkers board. The method has no board evaluation function, no explicit learning rules and no human expertise at playing checkers is used. The results show that, after a number of generations, by playing each other the agents begin to play much better and can easily beat agents that occur in earlier generations. Such learning abilities are encoded at a genetic level rather than at the phenotype level of neural connections.

Cartesian Genetic Programming (CGP) is a well-known form of Genetic Programming developed by Julian Miller in 1999-2000. In its classic form, it uses a very simple integer address-based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). It can handle cyclic or acyclic graphs. In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program. The classical form of CGP has undergone a number of developments which have made it more useful, efficient and flexible in various ways. These include self-modifying CGP (SMCGP), cyclic connections (recurrent-CGP), encoding artificial neural networks and automatically defined functions (modular CGP). SMCGP uses functions that cause the evolved programs to change themselves as a function of time. This makes it possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on). Recurrent-CGP allows evolution to create programs which contain cyclic, as well as acyclic, connections. This enables application to tasks which require internal states or memory. It also allows CGP to create recursive equations. CGP encoded artificial neural networks represent a powerful training method for neural networks. This is because CGP is able to simultaneously evolve the networks connections weights, topology and neuron transfer functions. It is also compatible with Recurrent-CGP enabling the evolution of recurrent neural networks. The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains. It will present a live demo of how the open source cgplibrary can be used.

Biology presents incomparable, but desirable, characteristics compared to engineered systems. Inspired by biological development, we have devised a multi-layered design architecture that attempts to capture many of the favorable characteristics of biological mechanisms for application to design problems. In this paper we have identified and implemented essential features of Genetic Regulatory Networks (GRNs) and cell signaling so that our system exhibits self-organization which is reminiscent of aspects of biological systems.

This work is part of a project that aims to develop and operate integrated evolvable hardware systems using unconstrained evolution. Experiments are carried out on an evolvable hardware platform featuring both combinatorial and registered logic as well as sequential feedback loops. In order to be able to accurately assess the transient output of the system and at the same time speed up evolution, new fitness evaluation methods are introduced. These bitwise and hierarchical fitness evaluation methods are adapted and further developed specifically for hardware implementation. It is shown that the newly developed approaches are particularly powerful in coping with two important issues: computational ambiguities, which generally occur when evaluating binary strings, and transient effects resulting from measuring hardware output. On two combinatorial problems it is shown that the new fitness functions improve the performance of evolution and allow stable solutions to be found more reliably. The experiments are carried out with a recently developed hardware platform called reconfigurable integrated system array (RISA).

The majority of artificial neural networks are static and lifeless and do not change themselves within a learning environment. In these models learning is seen as the process of obtaining the strengths of connections between neurons (i.e. weights). We refer to this as the 'synaptic dogma'. This is in marked contrast with biological networks which have time dependent morphology and in which practically all neural aspects can change or be shaped by mutual interactions and interactions with an external environment. Inspired by this and many aspects of neuroscience, we have designed a new kind of neural network. In this model, neurons are represented by seven evolved programs that model particular components and aspects of biological neurons (dendrites, soma, axons, synapses, electrical and developmental behaviour). Each network begins as a small randomly generated network of neurons. When the seven programs are run, the neurons, dendrites, axons and synapses can increase or decrease in number and change in interaction with an external environment. Our aim is to show that it is possible to evolve programs that allow a network to learn through experience (i.e. encode the ability to learn). We report on our continuing investigations in the context of learning how to play checkers.

A coevolutionary competitive learning environment for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based on a compartmentalised model of neurons. We have taken the view that the genetic basis of neurons is an important [27] and neglected aspect of previous approaches. Accordingly, we have defined a collection of chromosomes representing various aspects of the neuron: soma, dendrites and axon branches, and synaptic connections. Chromosomes are represented and evolved using a form of genetic programming known as Cartesian Genetic Programming. The network formed by running the chromosomal programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change in response to environmental interactions. The idea of this paper is to demonstrate the importance of the genetic transfer of learned experience and life time learning. The learning is a consequence of the complex dynamics produced as a result of interaction (coevolution) between two intelligent agents. Our results show that both agents exhibit interesting learning capabilities.

Classical Evolutionary Programming (CEP) and Fast Evolutionary Programming (FEP) have been applied to real-valued function optimisation. Both of these techniques directly evolve the real-values that are the arguments of the real-valued function. In this paper we have applied a form of genetic programming called Cartesian Genetic Programming (CGP) to a number of real-valued optimisation benchmark problems. The approach we have taken is to evolve a computer program that controls a writing-head, which moves along and interacts with a finite set of symbols that are interpreted as real numbers, instead of manipulating the real numbers directly. In other studies, CGP has already been shown to benefit from a high degree of neutrality. We hope to exploit this for real-valued function optimisation problems to avoid being trapped on local optima. We have also used an extended form of CGP called Embedded CGP (ECGP) which allows the acquisition, evolution and re-use of modules. The effectiveness of CGP and ECGP are compared and contrasted with CEP and FEP on the benchmark problems. Results show that the new techniques are very effective.

Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) capable of acquiring, evolving and re-using partial solutions. In this paper, we apply for the first time CGP and ECGP to the ones-max and order-3 deceptive problems, which are normally associated with Genetic Algorithms. Our approach uses CGP and ECGP to evolve a sequence of commands for a tape-head, which produces an arbitrary length binary string on a piece of tape. Computational effort figures are calculated for CGP and ECGP and our results compare favourably with those of Genetic Algorithms.

Prime generating polynomial functions are known that can produce sequences of prime numbers (e.g. Euler polynomials). However, polynomials which produce consecutive prime numbers are much more difficult to obtain. In this paper, we propose approaches for both these problems. The first uses Cartesian Genetic Programming (CGP) to directly evolve integer based prime-prediction mathematical formulae. The second uses multi-chromosome CGP to evolve a digital circuit, which represents a polynomial. We evolved polynomials that can generate 43 primes in a row. We also found functions capable of producing the first 40 consecutive prime numbers, and a number of digital circuits capable of predicting up to 208 consecutive prime numbers, given consecutive input values. Many of the formulae have been previously unknown.

Although intrinsic evolution has been shown to be capable of exploiting the physical properties of materials to solve problems, most researchers have chosen to limit themselves to using standard electronic components. However, it has been previously argued that because such components are human designed and intentionally have predictable responses, they may not be the most suitable medium to use when trying to get a naturally inspired search technique to solve a problem. Indeed allowing computer controlled evolution (CCE) to manipulate novel physical media can allow much greater scope for the discovery of unconventional solutions. Last year the authors demonstrated, for the first time, that CCE could manipulate liquid crystal to perform signal processing tasks (i.e frequency discrimination). In this paper we show that CCE can use liquid crystal to solve the much harder problem of controlling a robot in real time to navigate in an environment to reach an obstructed destination point.

An intrinsic evolvable hardware platform was realized to accelerate the evolutionary search process of a biologically inspired developmental model targeted at off-shelf FPGA implementation. The model has the capability of exhibiting very large transient fault-tolerance. The evolved circuits make up a digital "organism" from identical cells which only differ in internal states. Organisms implementing a 2-bit multiplier were evolved that can "recover" from almost any kinds of transient faults. This paper focuses on the design concerns and details of the evolvable hardware system, including the digital organism/cell and the intrinsic FPGA-based evolvable hardware platform.

Embedded Cartesian Genetic Programming (ECGP) is an extension of Cartesian Genetic Programming (CGP) that can automatically acquire, evolve and re-use partial solutions in the form of modules. In this paper, we introduce for the first time a new multi-chromosome approach to CGP and ECGP that allows difficult problems with multiple outputs to be broken down into many smaller, simpler problems with single outputs, whilst still encoding the entire solution in a single genotype. We also propose a multi-chromosome evolutionary strategy which selects the best chromosomes from the entire population to form the new fittest individual, which may not have been present in the population. The multi-chromosome approach to CGP and ECGP is tested on a number of multiple output digital circuits. Computational Effort figures are calculated for each problem and compared against those for CGP and ECGP. The results indicate that the use of multiple chromosomes in both CGP and ECGP provide a significant performance increase on all problems tested.

Embedded Cartesian Genetic Programming (ECGP) is an extension of the directed graph based Cartesian Genetic Programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the well known Lawnmower problem and to the Hierarchical-if-and-Only-if problem. The latter is normally associated with Genetic Algorithms. Computational effort figures are calculated from the results of both CGP and ECGP and our results compare favourably with other techniques.

Structure-based virtual screening is a technology increasingly used in drug discovery. Although successful at estimating binding modes for input ligands, these technologies are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of CGP for this problem and confirm the absence of bloat and the usefulness of neutral drift.

Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this paper we compare the efficiencies of the ECGP and CGP techniques on three classes of problem: digital adders, digital multipliers and digital comparators. We show that in most cases ECGP shows a substantial improvement in performance over CGP and that the computational speedup is more pronounced on larger problems.

Intrinsic evolution in evolvable hardware research has hitherto been limited to using standard electronic components as the media for problem solving. However, recently it has been argued that because such components are human designed and intentionally has predictable responses; they may not be the optimal medium to use when trying to get a naturally inspired search technique to solve a problem. Evolution has been demonstrated as capable of exploiting the physical properties of material to form solutions; however, by giving evolution only conventional components, we may be placing arbitrary constraints on our ability to solve certain problems. We have shown for the first time, that liquid crystal can be used as the physical substrate for evolution. We demonstrate that it is possible to evolve various functions, including a tone discriminator, in materio.

Embedded Cartesian Genetic Programming (ECGP) is a form of Genetic Programming based on an acyclic directed graph representation. In this paper we investigate the use of ECGP together with a technique called Product Reduction (PR) to reduce the time required to evolve a digital multiplier. The results are compared with Cartesian Genetic Programming (CGP) with and without PR and show that ECGP improves evolvability and also that PR improves the performance of both techniques by up to eight times on the digital multiplier problems tested.

Intrinsic evolution is often limited to using standard electronic components as the media for problem solving. It has been argued that because such components are human designed and intentionally have predictable responses, they may not be the optimal medium to use when trying to get a naturally inspired search technique to solve a problem. Evolution has been demonstrated as capable of exploiting the physical properties of material to form solutions, however, by giving evolution only conventional components, we may be limiting ourselves to solving certain problems. It is hoped by allowing evolution to explore a physically rich environment, it will be able to find novel solutions to tasks presented. This paper investigates the use of liquid crystal as a novel substrate for evolution and demonstrates the feasibility of moving beyond the silicon box.

A method for evolving programs that construct multicellular structures (organisms) is described. The paper concentrates on the difficult problem of evolving a cell program that constructs a fixed size French flag. We obtain and analyze an organism that shows a remarkable ability to repair itself when subjected to severe damage. Its behaviour resembles the regenerative power of some living organisms.

The paper presents for the first time automatic module acquisition and evolution within the graph based Cartesian Genetic Programming method. The method has been tested on a set of even parity problems and compared with Cartesian Genetic Programming without modules. Results are given that show that the new modular method evolves solutions up to 20 times quicker than the original non-modular method and that the speedup is more pronounced on larger problems. Analysis of some of the evolved modules shows that often they are lower order parity functions. Prospects for further improvement of the method are discussed.

Prefacio.-- El pdf es la versión post-print.

In this chapter we discuss the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this “challenge of complexity” in Genetic Programming.

An evolvable motherboard is an intrinsic evolution platform that allows the detailed probing of internal signals of circuits that have been evolved. Several designs for an evolvable motherboard have already been demonstrated to work successfully as a platform for the evolution of electronic circuits. This paper proposes a new platform that is suitable for intrinsic evolution using a wider variety of media. The platform presents a more modular design, making it suitable for use in evolving more complex physical primitives whilst affording the possibility of performing evolution in parallel for simpler problems. The construction of the device is discussed and examples of potential experiments in silicon, liquid crystal and other media are described.

It is argued that natural evolution is, par excellence, an algorithm that exploits the physical properties of materials. Such an exploitation of the physical characteristics has already been demonstrated in intrinsic evolution of electronic circuits. This paper is an attempt to point the way toward the exciting possibility of using artificial evolution to directly exploit the properties of materials, possibly at a molecular level. It is suggested that this may be best accomplished in materials not normally associated with electronic functions. Electronic components have been prefected by human designers to construct circuits using the traditional top-down methodology. Workers in artificial intrinsic hardware evolution have with the best of motives, been abusing such components. It is a tribute to the amazing resourcefulness of a blind evolutionary process that it has been possible to evolve new circuits in this way. Artificial evolution may be much more effective when the configurable medium has a rich and complicated physics. This idea is discussed and particular examples that look extremely promising are given. Ultimately it may be possible to evolve entirely new technologies and new sorts of computational systems may be devised that confer many advantages over conventional electronic technology.