A new book Essentials of Metaheuristics by Sean Luke is available online. The book can be downloaded for free on its web site. Information about the book from the author’s web site:
This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. It was developed as a series of lecture notes for an undergraduate course I taught at GMU. The chapters are designed to be printable separately if necessary. As it’s lecture notes, the topics are short and light on examples and theory. It’s best when complementing other texts. With time, I might remedy this.
At the SIGEVO Meeting at GECCO-2009, the GECCO-99 paper introducing the Bayesian optimization algorithm (BOA) was one of the two papers that received GECCO Impact Award. This is a new award and it focuses on past GECCO papers that have made most impact and have had most citations. The paper was in fact a project from David Goldberg’s class Genetic Algorithms in Search, Optimization, and Machine Learning (GE-485) at the University of Illinois at Urbana-Champaign.
The two awarded papers were:
M. Pelikan, D. Goldberg, E. Cantu-Paz (1999). BOA: The
Bayesian Optimization Algorithm. GECCO-99.
S. Hofmeyer, S. Forrest (1999). Immunity by Design: An
Artificial Immune System. GECCO-99.
I just put the slides from my GECCO-2009 presentations online both on the MEDAL Publications page and on the slideshare.net. The slideshare.net versions are embedded below:
The new issue of SIGEVOlution is now available for you to download from http://www.sigevolution.org. For me, the main highlight of the issue is the interview with John H. Holland with an introduction by Lashon Booker.
John H. Holland will give a keynote speech at GECCO-2009 on July 12, 2009 (Sunday), 10:40am-11:40am. The talk is entitled Genetic Algorithms: Long Ago [Past] and Far Away [Future] and the abstract of the talk follows:
It was in the mid-50’s of the 20th century when I realized that Fisher’s fundamental theorem could be extended from individual alleles to co-adapted sets of alleles, without linearization. That led to a realization that recombination, rather than mutation, was the main mechanism providing grist for the natural selection mill. There was little theory concerning recombination in those days, but now recombination is a standard explanation for biological innovations, such as swine flu.
Much later, in the early 1990’s, GA’s provided the “adaptive” part of rule-based models of complex adaptive systems (CAS), such as the artificial stock market pioneered at the Santa Fe Institute. Tag-based signal processing occurs in systems as different as biological cells, language acquisition, and ecosystems. CAS models offer a unified way to study the on-going co-evolution of boundary and tag networks in these systems.
Another keynote speaker at GECCO-2009 is Demetri Terzopoulos, who will give the talk Artificial Life Simulation of Humans and Lower Animals: From Biomechanics to Intelligence on July 11 (Saturday) at 4.10pm-5.50pm. As if this wasn’t enough, GECCO-2009 will also feature an invited talk of Hans-Paul Schwefel at the Learning from Failures in Evolutionary Computation (LFFEC) Workshop, which is entitled Failures as stepping stones to success or per aspera ad astra.
Pier Luca Lanzi just posted a few video highlights from the Simulated Car Racing Competition at CEC-2009. The winner of the competition was Thies Lonneker and Martin Butz. Congratulations!
The flow shop scheduling problem, or the problem of assignment of times to a set of jobs for processing through a series of machines, is NP-complete and has long received the attention of researchers in operations research, engineering, and computer science. Over the past several years, there has been a spurt of interest in “intelligent” heuristics and metaheuristics for solving this problem — ranging from genetic algorithms to tabu search to complex hybrid techniques. This talk discusses some of the newest approaches to this problem, their shortcomings, and directions for future research.
The title of the talk is failures as stepping stones to success or per aspera ad astra. The abstract follows:
The implicit thesis of this talk’s title will be underpinned with some examples from (my) real life. A first example leads back to the 1960s, when I simulated the (1+1)-ES with discrete mutations on a two-dimensional parabolic ridge by means of a Z23 computer. The result - getting stuck in certain search directions - led to making use of Gaussian variations. The second example comes from experimental investigations to determine the shape of a hot water flashing nozzle, the water being really hot and not simulated on a computer. In search for a multimembered evolutionary algorithm with effective self-adaptation of the mutation strengths, a couple of failures occurred. These, however, rendered deep insight into basic prerequisites to achieve the goal. And finally, some theory will be re-presented about the optimal failure rate in two black-box situations.
