An evolvable artificial cell is a chemical or biological complex system assembled in laboratory. The system is rationally designed to show life-like properties. In order to achieve an optimal design for the emergence ...
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An evolvable artificial cell is a chemical or biological complex system assembled in laboratory. The system is rationally designed to show life-like properties. In order to achieve an optimal design for the emergence of minimal life, a high dimensional space of possible experimental combinations can be explored. A machine learning approach (Evo-DoE) could be applied to explore this experimental space and define optimal interactions according to a specific fitness function. Herein an implementation of an evolutionary design of experiments to optimize chemical and biochemical systems based on a machine learning process is presented. The optimization proceeds over generations of experiments in iterative loop until optimal compositions are discovered. The fitness function is experimentally measured every time the loop is closed. Two examples of complex systems, namely a liposomal drug formulation and an in vitro cell-free expression system are presented as examples of optimization of molecular interactions in high dimensional space of compositions [1] , [4] . These represent, for instance, the modules or subsystems that could be optimized by “mixing the protocols” to achieve the high level of sophistication that artificial cells requires. In addition a replication cycle of oil in water emulsions is presented. They represent the container for the artificial cells.
In this article, we present an asynchronous version of the predators and prey pursuit domain by which we introduce a tradeoff between 'fast action' and 'complex computation'. The validity of past resul...
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In this article, we present an asynchronous version of the predators and prey pursuit domain by which we introduce a tradeoff between 'fast action' and 'complex computation'. The validity of past results on the synchronous version is verified in this new setting by considering four prominent kinds of prey from the literature: still prey, randomly moving prey, avoiding prey, and linear prey. Additionally, we introduce the linear prey with switching behavior. An evolutionary programming technique is used to evolve teams of predators whose capture rates are compared to that of a greedy strategy. The behavior of a single predator is defined by using a rule language, each team member has its own rule set. A simple, but explicit communication mechanism is used as a means of coordination in the evolved teams of predators. We conclude that evolved teams with explicit communication outperform greedy non-cooperative strategies when more competent prey is faced.
Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide ...
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Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.
This paper describes the assessment of dynamic Available Transfer Capability (ATC) by considering transient rotor angle stability. Since the loads and wheeling transactions vary dynamically in the deregulated power ma...
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This paper describes the assessment of dynamic Available Transfer Capability (ATC) by considering transient rotor angle stability. Since the loads and wheeling transactions vary dynamically in the deregulated power market, the power producers must respond quickly to those changes without affecting the system security and stability. This paper presents an application of the evolutionary programming (EP) algorithm to determine the optimal generation dispatch of power producers with dynamic security constraints. The proposed algorithm is demonstrated to determine dynamic ATC for practical IEEE-30 and Indian utility-62 bus systems with changing load conditions.
A distributed approach to obtain a motion sequence of transport tables for cellular warehouse problem is shown. In the proposed approach, the tables are considered to be autonomous agents, and a built-in behavior func...
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A distributed approach to obtain a motion sequence of transport tables for cellular warehouse problem is shown. In the proposed approach, the tables are considered to be autonomous agents, and a built-in behavior function given by ANNs and the evolved problem-oriented connection weights navigate the agents to their specified goals. To detennine the agent to be moved, a measure of the priority to move is introduced. Through various numerical experiments, we show the applicability of the proposed method and examine the contribution of the evaluation function to the learning result of behavior function.
An algorithm is presented for projecting — at the amino acid level — the three-dimensional crystal structure of a protein molecule onto a planar surface. The scheme is topologically consistent: if two amino acid res...
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An algorithm is presented for projecting — at the amino acid level — the three-dimensional crystal structure of a protein molecule onto a planar surface. The scheme is topologically consistent: if two amino acid residues are closely juxtaposed in three-dimensional space, they remain so upon projection. Through such projections, a single resulting picture captures the spatial relations amongst a protein molecule's amino acids. Operationally, a genetic algorithm is used to "evolve" a parameter set which serves as input for a self-organizing Kohonen neural network responsible for the projection itself. A fitness function characterizing the quality of the projections is defined and maximized via the genetic algorithm. The workings of both the genetic algorithm and neural network are discussed in detail. In this work, we seek to optimize projections resulting from the inherently "frustrated" task of collapsing a space-filling collection of amino acid residues onto a simpler surface. Ultimately, the chosen application is a testing ground for establishing the success of our coupled genetic algorithm/Kohonen neural network scheme which can easily be adapted for other uses.
A recent technology of information transmission in the fiber optics area. Wavelength Converters are key components in advanced WDM networks. Optical-transmission techniques have been researched for quite some time, Op...
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ISBN:
(纸本)9781479980819
A recent technology of information transmission in the fiber optics area. Wavelength Converters are key components in advanced WDM networks. Optical-transmission techniques have been researched for quite some time, Optical "networking" studies have been conducted only over the past dozen years or so. The field has matured enormously over this time. A large number of start ups have been formed, and Optical WDM technology is being deployed in the marketplace at a very rapid. In WDM networks, an optical fiber can carry several simultaneous wavelength channels. Each wavelength has to be different in the same fiber. The number of wavelengths that each fiber can carry is limited by the physical characteristic of the fiber and the state of optical technology, which is used to combine these wavelengths onto to the fiber and isolate them of the fiber. This motivated to do the research in the wavelength converter placement problem. The success of WDM networks depends heavily on the available optical device technology.
Commonly standard induction machines are used for both constant speed (CS) and variable speed (VS) wind power generation. But the operational conditions of an induction machine for VS wind power generation are differe...
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Commonly standard induction machines are used for both constant speed (CS) and variable speed (VS) wind power generation. But the operational conditions of an induction machine for VS wind power generation are different from CS wind power generation and motor applications. This paper considers the operating condition of VS wind energy conversion system (WECS) in maximum power tracking mode for the exclusive design of squirre-cage induction generator for VSWECS. In such a case, the induction machine always operate at a point close to the maximum torque and maximum efficiency. As a result, these maximums can be introduced to the sizing equations in place of conventionally defined rated efficiency, power factor and starting torque. This design strategy leads to downsizing of induction machine without sacrificing its capacity and performance. evolutionary programming in MATLAB 6.5 platform was used as a design optimization tool.
This paper proposes a solution to generator bidding strategy using a novel hybrid evolutionary Game Theory (EGT) and Differential Evolution (DE) method. In restructured power system, the generating companies (GENCOs) ...
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This paper proposes a solution to generator bidding strategy using a novel hybrid evolutionary Game Theory (EGT) and Differential Evolution (DE) method. In restructured power system, the generating companies (GENCOs) have an opportunity to compete in energy and ancillary services markets and earn profits. This competition creates a complicated situation to System Operator (SO) in the market clearing process. This paper attempts to maximize GENCOs profit with incomplete information by adopting optimal bidding strategies in energy and ancillary service markets while considering unit commitment constraints. Supply Function Equilibrium (SFE) model is employed to compute GENCOs profit. Nash Equilibrium points were calculated in the first stage by using evolutionary Game Theory and then optimal bidding strategies were found with the help of Differential Evolution method. evolutionary Game Theory is best suited for GENCOs bidding strategies but leads to slow convergence due to a large number of variables. So, a novel hybrid method involving evolutionary Game Theory with Differential Evolution is proposed in this paper. The proposed method to solve bidding strategies is employed on WSCC 9 and New England 39 bus test systems to demonstrate its merits.
A half-day tutorial session was given on evolutionary computation techniques and their applications to power system optimisation. The purpose of this course is to provide participants with basic knowledge of evolution...
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A half-day tutorial session was given on evolutionary computation techniques and their applications to power system optimisation. The purpose of this course is to provide participants with basic knowledge of evolutionary computation techniques, and how they are combined with knowledge elements in computational intelligence systems. Applications to power problems are stressed, and example applications are presented. The tutorial is composed of two parts: The first part gives an overview of evolutionary computation techniques, including fundamentals of genetic algorithms, evolutionary programming and strategies, particle swarm optimization, and ant colony search algorithm, and the hybrid system of evolutionary computation. It also gives an overview of power system applications.
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