Learning and Evolution

In the last few years several researchers have resorted to artificial evolution (e.g., genetic algorithms) and learning techniques (e.g., neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two different purposes: (a) looking at the performance advantages obtained by combining these two adaptive techniques; (b) understanding the role of the interaction between learning and evolution in biological organisms. In this paper we describe some of the most representative experiments conducted in this area and point out their implications for both perspectives outlined above. Understanding the interaction between learning and evolution is probably one of the best examples in which computational studies have shed light on problems that are difficult to study with the research tools employed by evolutionary biology and biology in general. From an engineering point of view, the most relevant results are those showing that adaptation in dynamic environments gains a significant advantage by the combination of evolution and learning. These studies also show that the interaction between learning and evolution deeply alters the evolutionary and the learning process themselves, offering new perspectives from a biological point of view. The study of learning within an evolutionary perspective is still in its infancy and in the forthcoming years it will produce an enormous impact on our understanding of how learning and evolution operate.

[1]  J. Baldwin.  A New Factor in Evolution , 1896, The American Naturalist.

[2]  C. Waddington.  Canalization of Development and the Inheritance of Acquired Characters , 1942, Nature.

[3]  L. V. Valen,et al.  A new evolutionary law , 1973 .

[4]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[5]  John Maynard Smith,et al.  When learning guides evolution , 1987, Nature.

[6]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[7]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[8]  John F. Kolen,et al.  Backpropagation is Sensitive to Initial Conditions , 1990, Complex Syst..

[9]  Richard K. Belew,et al.  Evolving networks: using the genetic algorithm with connectionist learning , 1990 .

[10]  Peter Dayan,et al.  Optimal Plasticity from Matrix Memories: What Goes Up Must Come Down , 1990, Neural Computation.

[11]  David H. Ackley,et al.  Interactions between learning and evolution , 1991 .

[12]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[13]  Peter M. Todd,et al.  Exploring adaptive agency II: simulating the evolution of associative learning , 1991 .

[14]  Stefano Nolfi,et al.  Auto-teaching: networks that develop their own teaching input , 1993 .

[15]  Francesco Mondada,et al.  Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms , 1993, ISER.

[16]  Piero Mussio,et al.  Toward a Practice of Autonomous Systems , 1994 .

[17]  Randall D. Beer,et al.  Sequential Behavior and Learning in Evolved Dynamical Neural Networks , 1994, Adapt. Behav..

[18]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[19]  Karl Sims,et al.  Evolving 3D Morphology and Behavior by Competition , 1994, Artificial Life.

[20]  D. Parisi,et al.  Phenotypic plasticity in evolving neural networks , 1994, Proceedings of PerAc '94. From Perception to Action.

[21]  L. Darrell Whitley,et al.  Lamarckian Evolution, The Baldwin Effect and Function Optimization , 1994, PPSN.

[22]  Jeffrey L. Elman,et al.  Learning and Evolution in Neural Networks , 1994, Adapt. Behav..

[23]  Inman Harvey.  Relearning and Evolution in Neural Networks , 1995, Adapt. Behav..

[24]  Filippo Menczer,et al.  Maturation and the Evolution of Imitative Learning in Artificial Organisms , 1995, Adapt. Behav..

[25]  Jim Kay,et al.  The discovery of structure by multi-stream networks of local processors with contextual guidance , 1995 .

[26]  Stefano Nolfi,et al.  Learning to Adapt to Changing Environments in Evolving Neural Networks , 1996, Adapt. Behav..

[27]  Inman Harvey,et al.  Unicycling Helps Your French: Spontaneous Recovery of Associations by Learning Unrelated Tasks , 1996, Neural Computation.

[28]  Inman Harvey,et al.  Is There Another New Factor in Evolution? , 1996, Evolutionary Computation.

[29]  Dave Cliff,et al.  Co-evolution of pursuit and evasion II: Simulation Methods and results , 1996 .

[30]  Giles Mayley,et al.  Landscapes, Learning Costs, and Genetic Assimilation , 1996, Evolutionary Computation.

[31]  Pattie Maes,et al.  Co-evolution of Pursuit and Evasion II: Simulation Methods and Results , 1996 .

[32]  Francesco Mondada,et al.  Evolution of Plastic Neurocontrollers for Situated Agents , 1996 .

[33]  Takahiro Sasaki,et al.  Adaptation toward Changing Environments: Why Darwinian in Nature? , 1997 .

[34]  Stefano Nolfi,et al.  God Save the Red Queen! Competition in Co-Evolutionary Robotics , 1997 .

[35]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.

[36]  Stefano Nolfi,et al.  Co-evolving predator and prey robots , 1998, Artificial Life.

[37]  Stefano Nolfi.  How Learning and Evolution Interact: The Case of a Learning Task which Differs from the Evolutionary Task , 1999, Adapt. Behav..

[38]  Stefano Nolfi,et al.  Extracting Regularities in Space and Time Through a Cascade of Prediction Networks: The Case of a Mobile Robot Navigating in a Structured Environment , 1999, Connect. Sci..

[39]  D. Floreano,et al.  Adaptive Behavior in Competing Co-Evolving Species , 2000 .

[40]  B. Calcott.  Learning and Evolution , 2022 .