The design (synthesis) of analog electrical circuits starts with a highlevel statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the problem of analog circuit synthesis is well known and there is no previously known general automated technique for synthesizing an analog circuit from a high-level statement of the circuit's desired behavior. This paper presents a single uniform approach using genetic programming for the automatic synthesis of both the topology and sizing of a suite of eight different prototypical analog circuits, including a lowpass filter, a crossover (woofer and tweeter) filter, a source identification circuit, an amplifier, a computational circuit, a timeoptimal controller circuit, a temperature-sensing circuit, and a voltage reference circuit. The problem-specific information required for each of the eight problems is minimal and consists primarily of the number of inputs and outputs of the desired circuit, the types of available components, and a fitness measure that restates the highlevel

We build the reasoning tool of metalanguage support (MS) with the embedded mechanism of the invention of predicates and metapredicates for the comprehension and synthesis tasks. This tool, based on metalanguage representation with weakened soundness, gains the inference flexibility to represent the formal scenarios (for example, a limited class of "logical" anecdotes, derived from NL). We introduce the concept of formal scenario as an alternative to the traditional axiomatic method (logical program) for the practical applications and develop specific MS means to perform the reasoning within a formal scenario. The anecdote scenario is an appealing object of study in the logical programming because it reflects the top level of human intellectual activity on one hand and is rather compact on the other. We present such applications as scenario comprehension and generation, genetic algorithm of MS formula modifications, NL aspects of scenarios and modeling of the psychological disorder with...

SoloPong / allows users anywhere to play the Solo Pong game. We compare people’s performance to a hand coded “Optimal ” player and programs automatically produced by computational intelligence. The computational intelligence techniques are: genetic programming, including a hybrid of GP and a human designed algorithm, and a particle swarm optimiser. The computational intelligence approaches are not fine tuned. GP and PSO find good players. Evolutionary computation (EC) is able to beat both human designed code and human players.