Despite nature's bewildering complexity, the driving force behind it is incredibly simple. 'Survival of the fittest' is an uncomplicated but brutally effective optimization strategy that has allowed life to solve complex problems, like vision and flight, and colonize the harshest of environments. Researchers are now trying to harness this optimization process to find solutions to a host of science and engineering problems. The idea of using evolutionary principles in computation dates back to the 1950s, but it wasn't until the 1960s that the idea really took off. By the 1980s the approach had crossed over from academic curiosities into real-world fields like engineering and economics.

The Genetic and Evolutionary Computation Conference (GECCO) presents the latest high-quality results in genetic and evolutionary computation since 1999. Topics include: genetic algorithms, genetic programming, ant colony optimization and swarm intelligence, complex systems (artificial life/robotics/evolvable hardware/generative and developmental systems/artificial immune systems), digital entertainment technologies and arts, evolutionary combinatorial optimization and metaheuristics, evolutionary machine learning, evolutionary multiobjective optimization, evolutionary numerical optimization, real world applications, search-based software engineering, theory and more.

In this paper, a genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of inter-class distance and intra-class distance. Moreover, the proposed feature search method can additionally search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable, thus, GAFDS exhibits good extensibility. Multiple classic classifiers (i.e., $k$-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Na\"ive Bayes) achieve good results by using the features generated by GAFDS method and the optimized selection. Specifically, the accuracies for the two-classification and three-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in feature extraction for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

To become a professional antenna designer, you can follow one of two paths: you can enroll in college- and graduate-level courses on electromagnetism, immerse yourself in the empirical study of antenna shapes, and apprentice yourself to an established technician willing to impart the closely guarded secrets of the discipline. Or you can do what Jason Lohn did: let evolution do the work. Physicists know a lot about Maxwell's equations and the other principles governing wireless communications. But antenna design is still pretty much a dark art, says Lohn, a computer scientist working at NASA Ames Research Center outside Mountain View, CA. "The field is so squirrelly. All your learning is through trial and error, the school of hard knocks."

Many of the most interesting problems in computer science, nano-technology, and synthetic biology require the construction of complex systems. But how would we build a really complex system – such as a general artificial intelligence (AI) that exceeded human intelligence? Some technologists advocate design; others prefer evolutionary search algorithms. Still others would conflate the two, hoping to incorporate the best of both while avoiding their limitations. But while both processes are powerful, they are very different and not easily combined.

Genetic programming has 16 attributes of what is sometimes called automatic programming or program synthesis or program induction). One of the central challenges of computer science is to get a computer to solve a problem without explicitly programming it. In particular, it would be desirable to have a problem-independent system whose input is a high-level statement of a problem's requirements and whose output is a working computer program that solves the given problem. "How can computers be made to do what needs to be done, without being told exactly how to do it?" Genetic programming is a biologically inspired, domain-independent method that automatically creates a computer program from a high-level statement of a problem's requirements.

One of the central challenges of computer science is to get a computer to do what needs to be done, without telling it how to do it. Genetic programming achieves this goal of automatic programming (also sometimes called program synthesis or program induction) by genetically breeding a population of computer programs using the principles of Darwinian natural selection and biologically inspired operations. The operations include reproduction, crossover (sexual recombination), mutation, and architecture-altering operations patterned after gene duplication and gene deletion in nature. Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally occurring genetic operations.

Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem. Genetic programming starts from a high-level statement of "what needs to be done" and automatically creates a computer program to solve the problem. There are now 36 instances where genetic programming has automatically produced a result that is competitive with human performance, including 15 instances where genetic programming has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, 6 instances where genetic programming has done the same with respect to a 21st-centry invention, and 2 instances where genetic programming has created a patentable new invention. Given these results, we say that "Genetic programming now routinely delivers high-return human-competitive machine intelligence." This statement is the most important point of the 2003 book Genetic Programming IV: Routine Human-Competitive Machine Intelligence.

Artificial Intelligence (AI) techniques are now being used by the practicing engineer to solve a whole range of hitherto intractable problems. This journal provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering. Engineering Applications of Artificial Intelligence publishes: • Survey papers/tutorials.

Taking a biologically inspired approach to the design of autonomous, adaptive machines. Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber. Non-members can purchase this article or a copy of the magazine in which it appears.