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Maintaining a stable residential water supply is becoming a challenge as climate change persists to affect environmental conditions. Thus, the use of forecasting tools is suggested as they can detect complications in water management and operation. Artificial neural network (ANN) model was established in this study to analyze the influence of each selected predictors of water supply in Metro Manila which were identified considering the country's condition. The model used socioeconomic surveys and historical climatic data to train and validate the model. Internal model parameters such as the Levenberg-Marquardt algorithm and hyperbolic tangent sigmoid function was utilized. The developed model has a topology of 9-19-1 (input-hidden-output) and it yielded extremely high R-values 0.97013 and very low mean square error of 2.3463. The data were also used in sensitivity analysis to identify the degree of significance for each predictor that are known to affect water usage in urban areas. Among the selected predictors, the household income holds the highest impact on water demand with SI of 1.346599. The number of female adults has the highest significance among the other demographic variables with 1.215813. Rainfall and temperature data must be paid attention to as well as they are in the 3rd and 4th rank, respectively. The model could be used as a basis to plan for the future and to understand how much water the region will need to continuously sustain daily individual and economic activities.

It would be desirable if computers could solve problems without the need for humans to write the detailed programmatic steps. That is, it would be desirable to have a domain independent automatic programming technique in which "What You Want Is What You Get" (WYWIWYG). Genetic programming is such a technique. This paper surveys three examples of problems (from the fields of cellular automata and molecular biology) in which genetic programming evolved a computer program that produced results that were slightly better than human performance for the same problem. This paper then discusses the problem of electronic circuit synthesis in greater detail. It shows how genetic programming can evolve both the topology of a desired electrical circuit and the sizing (numerical values) for each component in a crossover (woofer and tweeter) filter. Genetic programming has also evolved the design for a lowpass filter, the design of an amplifier, and the design for an asymmetric bandpass filter that was described as being difficult-to-design.

Rule-based systems used for optical character recognition (OCR) are notoriously difficult to write, maintain, and upgrade. The paper describes a method for using genetic programming (GP) to evolve and upgrade rules for an OCR system. The language of the evolved programs was designed such that human hand-coded rules can be included into the initial population in order to upgrade for a new font. The system was successful at learning rules for large character sets consisting of multiple fonts and sizes, with very good generalization to test sets. In addition, the method was found to be successful at updating hand-coded rules written in C for new fonts. This research demonstrates the successful application of GP to a difficult, noisy, real-world problem. >

An essential component of an intelligent agent is the ability to observe, encode, and use information about its environment. Traditional approaches to genetic programming have focused on evolving functional or reactive programs with only a minimal use of state. This paper presents an approach for investigating the evolution of learning, planning, and memory using genetic programming. The approach uses a multi-phasic fitness environment that enforces the use of memory and allows fairly straightforward comprehension of the evolved representations. An illustrative problem of 'gold' collection is used to demonstrate the usefulness of the approach. The results indicate that the approach can evolve programs that store simple representations of their environments and use these representations to produce simple plans. >

Analog electrical circuits that perform mathematical functions (e.g., cube root, square) are called computational circuits. Computational circuits are of special practical importance when the small number of required mathematical functions does not warrant converting an analog signal into a digital signal, performing the mathematical function in the digital domain, and then converting the result back to the analog domain. The design of computational circuits is difficult even for mundane mathematical functions and often relies on the clever exploitation of some aspect of the underlying device physics of the components. Moreover, implementation of each different mathematical function typically requires an entirely different clever insight. This paper demonstrates that computational circuits can be designed without such problem-specific insights using a single uniform approach involving genetic programming. Both the circuit topology and the sizing of all circuit components are created by genetic programming. This uniform approach to the automated synthesis of computational circuits is illustrated by evolving circuits that perform the cube root function (for which no circuit was found in the published literature) as well as for the square root, square, and cube functions.

As newly sequenced proteins are deposited into the world's ever growing archive of protein sequences, they are typically immediately tested by various computerized algorithms for clues as to their biological structure and function. One question about a new protein involves its cellular location-that is, where the protein resides in a living organism (extracellular, intracellular, etc.). A paper by J. Cedano et al. (1997) reported a human-created five way algorithm for cellular location created using statistical techniques with 76% accuracy. The article describes a two way classification algorithm that was evolved using genetic programming with 83% accuracy for determining whether a protein is extracellular. Unlike the statistical calculation, the genetically evolved algorithm employs a large and varied arsenal of computational capabilities, including arithmetic functions, conditional operations, subroutines, iterations, memory, data structures, set creating operations, macro definitions, recursion, etc. The genetically evolved classification algorithm can be viewed as an extension (which we call a programmatic motif) of the conventional notion of a protein motif. The genetically evolved program constitutes an instance of an evolutionary computation technique producing a solution to a problem that is competitive with that produced using human intelligence.

The problem of analog circuit design is a difficult problem that is generally viewed as requiring human intelligence to solve. Considerable progress has been made in automating the design of certain categories of purely digital circuits; however, the design of analog electrical circuits and mixed analog-digital circuits has not proved to be as amenable to automation. When critical analog circuits are required for a project, skilled and highly trained experts are necessary. Previous work on applying genetic programming to the design of analog circuits has proved to be successful at evolving a wide variety of circuits, including filters, amplifiers and computational circuits; however, these previous approaches have required the specification of an appropriate embryonic circuit. This paper explores a method to eliminate even this small amount of problem-specific knowledge, and, in addition, proves that the representation used is capable of producing all circuits.