Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoners Dilemma

Marks, Robert E.; Schnabl, Hermann

doi:10.1007/978-1-4615-5029-7_8

Cited by 5 publications

(12 citation statements)

References 13 publications

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“…The type used most frequently in modelling and simulation is the feed-forward network with backpropagation learning [1]. The network is given input-output pair examples and the back-propagation algorithm works to reduce the error of the program.…”

Section: Neural Networkmentioning

confidence: 99%

“…The widely adaptable nature of these algorithms allows them to solve problems in fields ranging from economics to medicine [1]. GAs solve these problems using curve fitting, a process by which a closed-form function is approximated in order to provide the line of best fit to a given data set.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…The genotype holds the blueprints for that organism's appearance and behaviour. The actual appearance and behaviour themselves are called the phenotype [1]. Given an initial population and an environmental pressure which favours a particular phenotype, the genetic code of the population will trend towards those genotypes which can produce the most suitable phenotypes for the environment.…”

Section: Development Of a Genetic Algorithmmentioning

confidence: 99%

“…Parents 1 and 2 were paired together to create two children, Parents 3 and 4 were paired together to create two children, and so on, so that the ten designated parent genotypes produced ten new genotypes to test with the system. The i th coefficients, or "chromosomes" of the children, were found using an arithmetic crossover of the i th chromosomes of the parents [1].…”

Section: Childrenmentioning

confidence: 99%

“…Mutations are a way of increasing the number of paths being explored in the solution space and decreasing the likelihood of convergence on a local optimum [1].…”

Section: Mutationsmentioning

confidence: 99%

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Automated Model Tuning Using A Genetic Algorithm

Swaine¹,

Langlois

2016

Proceedings of the 3rd International Conference of Control, Dynamic Systems, and Robotics (CDSR'16)

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-A simple but reliable model tuning method was developed in order to tune a flight model for a high-fidelity type-specific small aircraft simulator. A genetic algorithm (GA) was used as a parameter estimation method. GAs are robust parallel heuristic search methods that often use least squares curve fitting methods to solve complex problems. They belong to the class of evolutionary computing algorithms that mimic natural processes, in this case evolution, to predict behaviour and solve optimization problems. A population of possible solution sets is selected at random and the known math model is then used to determine the behaviour of each of these possible solutions. The behaviour of each is then compared to the desired behaviour of the model, i.e., the reference data set, and the error is calculated. Those with the highest error are culled from the population while those with the lowest error are deemed to be "parents". These parent solution sets are then paired together, to create "children" by finding a weighted average of the parents. To ensure the solution space is fully explored, "mutations" are also created by replacing a single part of select solution sets with a randomly-generated value. Two mutation mechanisms were used in the algorithm described in this paper to ensure that the solution space was explored fully while avoiding convergence on a local, rather than global, minimum. The second generation solution set is 50% comprised of parents, 25% comprised of children, and 25% comprised of mutations. The process repeats until the convergence criteria is met. This algorithm was successfully tested with multiple dynamic systems, including simulated flight test data created using X-Plane, a flight simulator software. The algorithm proved to be a capable and adaptive parameter estimation method applicable to a wide variety of dynamic models, including flight models. IntroductionFlight model tuning is an important step in the development of a high-fidelity type-specific aircraft simulator. In an effort to develop a low-cost high-fidelity Diamond Aircraft DA20-A1 Katana flight simulator, an automated method for extracting stability derivatives from flight data was developed using a simple, but powerful, genetic algorithm (GA). As will be shown, not only is the developed algorithm capable of tuning a flight model, but it is readily adaptable to potentially tune any system for which the governing equations of motion and the desired behaviour are known. This paper provides an overview of flight model tuning, describes the operation of genetic algorithms, details the development of the flight model tuning algorithm, presents sample results, and provides relevant discussion.

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Section: Neural Networkmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

Section: Development Of a Genetic Algorithmmentioning

confidence: 99%

Section: Childrenmentioning

confidence: 99%

“…Mutations are a way of increasing the number of paths being explored in the solution space and decreasing the likelihood of convergence on a local optimum [1].…”

Section: Mutationsmentioning

confidence: 99%

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Automated Model Tuning Using A Genetic Algorithm

Swaine¹,

Langlois

2016

Proceedings of the 3rd International Conference of Control, Dynamic Systems, and Robotics (CDSR'16)

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Downstream Processing Technologies/Capturing and Final Purification

Singh

Herzer

2017

Advances in Biochemical Engineering/Biotechnology

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Increased pressure on upstream processes to maximize productivity has been crowned with great success, although at the cost of shifting the bottleneck to purification. As drivers were economical, focus is on now on debottlenecking downstream processes as the main drivers of high manufacturing cost. Devising a holistically efficient and economical process remains a key challenge. Traditional and emerging protein purification strategies with particular emphasis on methodologies implemented for the production of recombinant proteins of biopharmaceutical importance are reviewed. The breadth of innovation is addressed, as well as the challenges the industry faces today, with an eye to remaining impartial, fair, and balanced. In addition, the scope encompasses both chromatographic and non-chromatographic separations directed at the purification of proteins, with a strong emphasis on antibodies. Complete solutions such as integrated USP/DSP strategies (i.e., continuous processing) are discussed as well as gains in data quantity and quality arising from automation and high-throughput screening (HTS). Best practices and advantages through design of experiments (DOE) to access a complex design space such as multi-modal chromatography are reviewed with an outlook on potential future trends. A discussion of single-use technology, its impact and opportunities for further growth, and the exciting developments in modeling and simulation of DSP rounds out the overview. Lastly, emerging trends such as 3D printing and nanotechnology are covered. Graphical Abstract Workflow of high-throughput screening, design of experiments, and high-throughput analytics to understand design space and design space boundaries quickly. (Reproduced with permission from Gregory Barker, Process Development, Bristol-Myers Squibb).

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Playing Games with Genetic Algorithms

Marks¹

2002

Evolutionary Computation in Economics and Finance

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Abstract. In 1987 the first published research appeared which used the Genetic Algorithm as a means of seeking better strategies in playing the repeated Prisoner's Dilemma. Since then the application of Genetic Algorithms to game-theoretical models has been used in many ways. To seek better strategies in historical oligopolistic interactions, to model economic learning, and to explore the support of cooperation in repeated interactions. This brief survey summarises related work and publications over the past thirteen years. It includes discussions of the use of gameplaying automata, co-evolution of strategies, adaptive learning, a comparison of evolutionary game theory and the Genetic Algorithm, the incorporation of historical data into evolutionary simulations, and the problems of economic simulations using real-world data.

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Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoners Dilemma

Cited by 5 publications

References 13 publications

Automated Model Tuning Using A Genetic Algorithm

Automated Model Tuning Using A Genetic Algorithm

Downstream Processing Technologies/Capturing and Final Purification

Playing Games with Genetic Algorithms

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