Frank, S. A. 1996. The design of natural and artificial adaptive systems. Pages 451-505 in Adaptation, M.R. Rose and G.V. Lauder, eds. Academic Press.
1. Introduction
The design of adaptive systems will be among the key research problems of the twenty-first century. This new field is emerging from several distinct lines of work.
- Modern immunology is based on the theory of clonal selection and adaptive immunity. The remarkable recognition abilities of the vertebrate immune system depend on the programmed mechanisms of antibody variation and selection that occur within each individual.
- The design of intelligent computer systems and robots depends on a balance between adaptive improvement by exploration and efficient exploitation of known solutions. Many of the current computer implementations use evolutionary algorithms to achieve adaptation to novel or changing environments.
- The adaptive response of genetic systems to environmental challenge depends strongly on the tempo and mode of sex and recombination. Sexual systems vary widely in nature. Which processes have shaped this variation is a major puzzle in evolutionary biology.
- Wiring a brain during development and using that brain to learn are great problems of information management. Recent studies in neuroscience suggest that programmed mechanisms of stochastic variation and controlled selection guide neural development and learning. If true, then nature has solved these informational problems by using somatic adaptive systems that are programmed to work in the same way as natural selection.
What do these different fields have in common? Will there be a new science of adaptation shared by biology and engineering? Can a unified theory guide the study of so many different phenomena? What will be the central tenets of such a theory?
These are difficult questions. To make a start I survey the range of adaptive systems as they are currently understood: adaptive immunity, learning, development, culture and symbiosis, the origin and evolution of genetic systems, and artificial adaptive systems in engineering. The facts that I present in my survey, fascinating in their own right, provide the database from which more general insights must be built.
2. Challenges to adaptive systems
Before starting on the survey it is useful to have a conceptual framework. I begin with a rough definition. An “adaptive system'“ is a population of entities that satisfy the three conditions of natural selection: the entities vary, they have continuity (heritability), and they differ in their success. The entities in the population can be genes, competing computer programs, or anything else that satisfies the three conditions.
In this section I propose a classification of the challenges that have shaped adaptive systems and the ways in which adaptive systems have responded to these challenges. A surprisingly small number of challenges and responses cover the main features of adaptive systems ranging from genetics to robotics. I illustrate the concepts with brief examples that will be discussed in more detail during the survey.
Information decay is one kind of challenge. For example, genetic systems suffer information decay when random mutations occur. If mutations accumulate too rapidly then adaptive improvement by natural selection is impossible. The population suffers an “error catastrophe” (Eigen 1992) or “mutational meltdown” (Lynch et al. 1993).
Predictable complexity is another type of challenge. For example, the information required to specify the point-to-point neural connections of a human brain greatly exceeds the amount of information encoded by the genome. Thus the genetic system must cope with the problem of creating a complex pattern during development from a relatively limited set of instructions.
Unpredictable challenges are the third type of problem faced by adaptive systems. For example, parasites vary unpredictably over space and time. To give an engineering example, a robot engaged in war cannot have prewired responses for all possible attack strategies that the enemies may use. A successful robot must adjust to unpredictable events.
I will argue during my survey that this small list—information decay, complexity and unpredictability—describes the main challenges faced by adaptive systems (Table 2.1). The next step is to consider how adaptive systems respond to these challenges (Table 2.2).
Table 2.1. Challenges to adaptive systems.
- Information Decay
- ubiquitous “tax” on information storage and transmission.
- Predictable complexity
- Challenge is to learn predictable pattern or achieve predictable form, but complexity of pattern greatly exceeds the available information storage.
- Unpredictable challenge
- (a) Environmental—abiotic challenges and biotic interactions without feedback.
- (b) Coevolutionary—biotic interactions with feedback between systems.
Enhancing transmission fidelity is one way to overcome the problem of information decay. For example, Bernstein et al. (1988) suggest that sex is the genetic system's way of enhancing transmission fidelity in response to the information decay imposed by mutation. In their theory sex brings together two different copies of the genetic material, which allows a damaged copy to be corrected by the undamaged copy.
The problem of balancing exploration versus exploitation recurs in all adaptive systems (Holland 1975). Exploration of new ways to solve problems often carries a cost because competitors may devote more energy to the efficient exploitation of known solutions. For example, sex increases genetic variability among offspring compared with asexual reproduction. Greater variability improves the chances that some of the offspring will have genotypes that match an unpredictable environment. Thus sexual systems may be a form of exploration, but this exploration is costly because asexuality is usually a more efficient mode of reproduction. There is much controversy among evolutionary biologists about whether sexual systems have evolved as a method of exploration in response to unpredictable challenge or as a method to enhance transmission fidelity in response to the to challenge of information decay.
Table 2.2. Responses to challenge.
- Transmission fidelity
- Mechanisms to reduce errors in the storage and transmission of encoded solutions.
- Exploration versus exploitation
- The balance between costly exploration for improved efficiency and the cheap exploitation of known solutions.
- Generative rules
- Simple rules to generate complex phenotypes. Genotypes do not specify explicit blueprints for structure.
- Instructional subsystem
- Mechanism to store information obtained directly from the environment.
- Adaptive subsystem
- A system of variation and selection “spawned” by an evolving system to solve a particular problem.
- Symbiosis
- Cooperation between separate evolving entities to achieve greater group efficiency. Conflicts among group members often arise.
The transmissible information (genotype) of an adaptive system often contains generative rules for the design of phenotypic structure (Thompson 1961; Lindenmayer 1971). In organisms each detail of morphology and behavior is not coded by an explicit DNA sequence; there is no blueprint for design. Fingerprints are generated by the biochemical rules of morphogenesis contained in the genome. Those rules may be fairly simple, but the outcome is complex and partly influenced by chance.
Simple environmental patterns may directly influence the internal information store through an instructional subsystem. For example, repeated stimulation of some neurons causes an increase in the stimulus required to evoke a response. The instructional subsystem takes a direct measure of environmental pattern.
Adaptive systems may spawn adaptive subsystems to handle difficult challenges (Gell-Mann 1994). For example, the immune system of vertebrates has a specialized set of mechanisms to generate variability among recognition molecules and a second set of mechanisms to select and amplify recognition molecules that react with invading parasites. These controls of the adaptive immune system are specified by the underlying genetic system, or, put another way, the genetic system has spawned an adaptive subsystem to handle the unpredictable challenges of parasitic attack. In later sections I will discuss certain aspects of development and learning as adaptive subsystems spawned by the genetic system.
Symbiosis is the living together of two or more dissimilar organisms. An interesting theory about the origin of life illustrates the importance of symbiosis (Eigen 1992). Information decay was a severe problem for the first replicating molecules because of high mutation rates. The mutation rate sets an “error threshold” that determines the upper limit on the size of informational molecules and thus the storage capacity of genetic systems. The early replicators were limited to very small genome sizes because of the error threshold. This creates a paradox: small genomes do not have sufficient information to code for an error-correcting replication machinery; without error correction larger genomes cannot evolve.
Symbiosis appears to be the solution. A set of small replicators, each below the error threshold, may have cooperated to produce error-correcting enzymes. This symbiotic group, with a reduced rate of transmission errors, could then increase in size and complexity.
Cooperation among early replicators was the first successful symbiosis. The most recent example of symbiosis in adaptive systems comes from research on robot design. Teamwork among robots boosts efficiency for tasks that require division of labor and specialization, such as automated manufacturing, search and rescue, or surveillance (Parker 1993). Both biological symbiosis and robot teamwork must resolve the tension between the autonomy of components and the control of the symbiotic group. I will discuss this problem for both genomes and robots in later sections.
I turn now to my survey of adaptive systems. I start with the transformation of genotype into phenotype. The first section describes vertebrate immunity, an adaptive subsystem of variation and selection that occurs within each individual's body. The following section considers the problems of neural development and learning. I raise the possibility that adaptive subsystems play a role in these complex informational processes. The final section of this group focuses on morphology. I contrast simple generative rules for development with more complex processes of developmental variation and selection.
After the genotype-phenotype transformations, I turn to the evolution of genetic systems. There is a natural tendency to view a genetic system as a stable, well-defined core of hereditary information. But each apparent system is actually a complex symbiosis of partly conflicting and partly cooperating hereditary systems. Each has its own pattern of continuity (transmission) and its own generative rules for the production of phenotype. Sex and recombination define one widespread pattern of hereditary mixture and symbiosis. I consider how sex fits into the recurring challenges and responses of adaptive systems outlined Tables 2.1 and 2.2.
The final section places some new aspects of human engineering in the framework of adaptive systems. At one level these new methods are simply the use of variation and selection as an engineering tool for problems such as robotics. The effective use of selection follows in many ways the design of natural adaptive systems. At another level the new forms of “artificial life,” with their new symbioses and their higher-order adaptive subsystems spawned by humans, are simply the next historical stage in the evolution of adaptive systems.
3. Adaptive immunity in vertebrates
- Positive selection and clonal expansion
- Diversity: somatic recombination and mutation
- Negative selection and self versus nonself discrimination
- Genetical evolution of adaptive immunity
4. Learning
- Instruction versus selection
- Neural darwinism
- Genetical evolution of learning
5. Development
- Morphology
- Developmental selection
- Summary
6. Symbiosis
- Culture
- Symbiosis in the genome
- The organism as a community
7. The origin and evolution of genetic systems
- The error threshold and the origin of life
- Symbiosis and early evolution
- Artificial life
- The evolution of sex
8. Adaptive systems as an engineering tool
- The design of biochemical catalysts by chemical engineers
- Genetic algorithms and protein folding
- Genetic algorithms and neural nets
- Hierarchical control and learning in robots
- Robot symbiosis
9. Conclusions
10. References