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.

Many and long were the conversations between Lord Byron and Shelley to which I was a devout and silent listener. During one of these, various philosophical doctrines were discussed, and among others the nature of the principle of life, and whether there was any probability of its ever being discovered and communicated. They talked of the experiments of Dr. Darwin (I speak not of what the doctor really did or said that he did, but, as more to my purpose, of what was then spoken of as having been done by him), who preserved a piece of vermicelli in a glass case till by some extraordinary means it began to move with a voluntary motion. Not thus, after all, would life be given. Perhaps a corpse would be reanimated; galvanism had given token of such things: perhaps the component parts of a creature might be manufactured, brought together, and endued with vital warmth (Butler 1998).

Tim Berry is president and founder of Palo Alto Software and bplans.com, Call it coincidence, serendipity, synchronicity, or just random, but last week I was accidentally exposed to two seemingly unrelated ideas that ended up seeming very related to me. And they gave me a fascinating whack on the side of the head. I thought artificial intelligence had run its course, but computers that learn could be much more important. First, the book Blondie24, by David Fogel, describing how he and his team used evolutionary computing to develop computer programming that taught itself to play checkers.

Engineers love to do crazy things and when they involve robots, we love to tell you about them. We've reported on a lot of ping-pong playing robots over the years but usually they're based on conventional industrial robot arms or humanoid arm designs. What if, instead of a multi-jointed arm, you wanted to design a cartesian ping-pong playing robot? That is, a robot that can only move linearly on an X, Y, and Z axis. That's the question Hossein Jahandideh and his fellow engineers asked themselves.

The AnyScale Learning For All (ALFA) Group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) aims to solve the most challenging big-data problems -- questions that go beyond the scope of typical analytics. ALFA applies the latest machine learning and evolutionary computing concepts to target very complex problems that involve high dimensionality. "People have data coming at them from so many different channels these days," says ALFA director Una-May O'Reilly, a principal research scientist at CSAIL. "We're helping them connect and link the data between those channels." The ALFA Group has taken on challenges ranging from laying out wind farms to studying and categorizing the beats in blood pressure data in order to predict drops and spikes.

The application of Evolutionary Computation (EC) techniques for the development of creative systems is a new, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: art and music generation, analysis and interpretation; architecture; and design. EvoMUSART 2005 is the third workshop of the EvoNet working group on Evolutionary Music and Art. Following the success of previous events, the main goal of EvoMUSART 2005 is to bring together researchers who are using Evolutionary Computation in this context, providing the opportunity to promote, present and discuss ongoing work in the area. The workshop will include an open panel for the discussion of the most relevant questions of the field.

The application of Evolutionary Computation (EC) techniques for the development of creative systems is a new, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: art and music generation, analysis and interpretation; architecture; and design. EvoMUSART 2006 is the third workshop of the EvoNet working group on Evolutionary Music and Art. Following the success of previous events, the main goal of EvoMUSART 2006 is to bring together researchers who are using Evolutionary Computation in this context, providing the opportunity to promote, present and discuss ongoing work in the area. The workshop will include an open panel for the discussion of the most relevant questions of the field.

Researchers from the Massachusetts Institute of Technology recently presented their new artificially intelligent system that can fill the information gap itself by surfing the Internet. During the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, MIT researchers said the AI system has the ability to gather structured information from unstructured machine readable documents automatically. Karthik Nasarimhan, one of the co-authors of the study, said that in order for them to do this, they employed a technique called reinforcement learning where the system learns through the notion of cumulative reward. This technique was based on behavioral psychology and is also used in swarm intelligence, game theory, and genetic algorithms among others. According to Nasarimhan, the technique is necessary because there is a lot of contrasting information out which can cause uncertainty when the data is merged.