Deadline extended for Special Issue on Parallel and Distributed Evolutionary Algorithms

The deadline for submitting papers to the Genetic Programming and Evolvable Machines Special Issue on Parallel and Distributed Evolutionary Algorithms has been extended.

The new deadline is: May 15, 2009

More information about the special issue is available here.

The deadline for submitting papers to the Genetic Programming and Evolvable Machines Special Issue on Parallel and Distributed Evolutionary Algorithms has been extended.

The new deadline is: May 15, 2009

More information about the special issue is available here.

Automated synthesis of resilient and tamper-evident analog circuits without a single point of failure

Abstract  This study focuses on the use of genetic programming to automate the design of robust analog circuits. We define two complementary
types of failure modes: partial short-circuit and partial disconnect, and demonstrated novel circuit…

Abstract  This study focuses on the use of genetic programming to automate the design of robust analog circuits. We define two complementary
types of failure modes: partial short-circuit and partial disconnect, and demonstrated novel circuits that are resilient across
a spectrum of fault levels. In particular, we focus on designs that are uniformly robust, and unlike designs based on redundancy,
do not have any single point of failure. We also explore the complementary problem of designing tamper-proof circuits that
are highly sensitive to any change or variation in their operating conditions. We find that the number of components remains
similar both for robust and standard circuits, suggesting that the robustness does not necessarily come at significant increased
circuit complexity. A number of fitness criteria, including surrogate models and co-evolution were used to accelerate the
evolutionary process. A variety of circuit types were tested, and the practicality of the generated solutions was verified
by physically constructing the circuits and testing their physical robustness.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-009-9085-2
  • Authors
    • Kyung-Joong Kim, Cornell University Mechanical and Aerospace Engineering Ithaca NY 14853 USA
    • Adrian Wong, Cornell University Electrical and Computer Engineering Ithaca NY 14853 USA
    • Hod Lipson, Cornell University Computing and Information Science 216 Upson Hall Ithaca NY 14853-7501 USA

The influence of mutation on population dynamics in multiobjective genetic programming

Abstract  Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but
can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we repo…

Abstract  Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but
can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed
examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair
and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean
and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role
in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation
controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore
the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary
approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the
population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-009-9084-3
  • Authors
    • Khaled Badran, University of Sheffield Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering Mappin Street Sheffield S1 3JD UK
    • Peter I. Rockett, University of Sheffield Laboratory for Image and Vision Engineering, Department of Electronic and Electrical Engineering Mappin Street Sheffield S1 3JD UK

Evolution of Modularity, GP, and a new PLoS Computational Biology paper by Kashtan et al.

A new paper by Kashtan et al. in PLoS Computational Biology presents an interesting study of the evolution of modularity, extending their previous work showing “that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals.”
The evolution of modularity is a topic of longstanding interest in GP and evolutionary computation more generally, within which we often seek to evolve modular programs or structures. Many also seek to leverage the modularity of representations to accelerate evolution. A lot of the work on automatically defined functions, etc., has been concerned with these issues and I think that cross-fertilization with the new computational biology results could be fruitful.
The closest thing that I know of in the GP literature to the Kashtan et al. results is a paper by Terry Van Belle and David Ackley in GECCO 2002, in which they observed the “evolution of evolvability in experiments using genetic programming to solve a symbolic regression problem that varies in a partially unpredictable manner.” Alan Robinson and I were inspired by this to do a similar experiment in PushGP, which allows modularity to arise from scratch via code self-manipulation, and we wrote it up briefly in a GECCO 2002 Workshop paper (see section 3.2).
A new paper by Kashtan et al. in PLoS Computational Biology presents an interesting study of the evolution of modularity, extending their previous work showing “that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals.”
The evolution of modularity is a topic of longstanding interest in GP and evolutionary computation more generally, within which we often seek to evolve modular programs or structures. Many also seek to leverage the modularity of representations to accelerate evolution. A lot of the work on automatically defined functions, etc., has been concerned with these issues and I think that cross-fertilization with the new computational biology results could be fruitful.
The closest thing that I know of in the GP literature to the Kashtan et al. results is a paper by Terry Van Belle and David Ackley in GECCO 2002, in which they observed the “evolution of evolvability in experiments using genetic programming to solve a symbolic regression problem that varies in a partially unpredictable manner.” Alan Robinson and I were inspired by this to do a similar experiment in PushGP, which allows modularity to arise from scratch via code self-manipulation, and we wrote it up briefly in a GECCO 2002 Workshop paper (see section 3.2).