3.3 Experimental Conditions 4.3 Varying Population Size In all experiments; 20,000 objective func... more 3.3 Experimental Conditions 4.3 Varying Population Size In all experiments; 20,000 objective function evaluations were made, regardless of the number of variables being searched-The experi ine rus were of the form (jj,43ll) or ()Х, 3u), ie, parents were selected at random to ...
IEEE Transactions on Evolutionary Computation, 1999
Complex adaptive systems have historically been studied using simplifications that mandate determ... more Complex adaptive systems have historically been studied using simplifications that mandate deterministic interactions between agents or instead treat their interactions only with regard to their statistical expectation. This has led to an anticipation, even in the case of agents employing inductive reasoning in light of limited information, that such systems may have equilibria that can be predicted a priori. This hypothesis is tested here using a simulation of a simple market economy in which each agent's behavior is based on the result of an iterative evolutionary process of variation and selection applied to competing internal models of its environment. The results indicate no tendency for convergence to stability or a longterm equilibrium and highlight fundamental differences between deterministic and stochastic models of complex adaptive systems.
Artificial neural networks are applied to the problem of detecting breast cancer from histologic ... more Artificial neural networks are applied to the problem of detecting breast cancer from histologic data. Evolutionary programming is used to train the networks. This stochastic optimization method reduces the chance of becoming trapped in locally optimal weight sets. Preliminary results indicate that very parsimonious neural nets can outperform other methods reported in the literature on the same data. The results are statistically significant.
Evolutionary algorithms, including evolutionary programming and evolution strategies, have often ... more Evolutionary algorithms, including evolutionary programming and evolution strategies, have often been applied to real-valued function optimization problems. These algorithms generally operate directly on the real values to be optimized, in contrast with genetic algorithms which usually operate on a separately coded transformation of the objective variables. Evolutionary algorithms often rely on a second-level optimization of strategy parameters, tunable variables that in part determine how each parent will generate offspring. Two alternative methods for performing this second-level optimization have been proposed and are compared across a series of function optimization tasks. The results appear to favor the approach offered originally in evolution strategies, although the applicability of the findings may be limited to the case where each parameter of a parent solution is perturbed independently of all others.
IEEE Transactions on Evolutionary Computation, 1997
Consideration is given to the effects of representations and operators in evolutionary algorithms... more Consideration is given to the effects of representations and operators in evolutionary algorithms. In particular, theorems are presented which establish, under some general assumptions, that no choice of cardinality of a representation offers any intrinsic advantage over another. Functionally equivalent algorithms can be constructed regardless of the chosen representation. Further, a similar effective equivalence of variation operators is shown such that no intrinsic advantage accrues to any particular one-parent operator or any particular two-parent operator.
A s the subtitle indicates, the book by David Fogel is not just a technical monograph about evolu... more A s the subtitle indicates, the book by David Fogel is not just a technical monograph about evolutionary computation, a subfield of computer science that deals with algorithms gleaned from the model of organic evolution, but also emphasizes a philosophical view of machine intelligence (artificial intelligence) and its relation to evolutionary processes. The definition of intelligence as "the capability of a system to adapt its behavior to meet its goals in a range of environments…" (p. 24) dates back to the '60s, when Lawrence J. Fogel (David's father) developed the evolutionary programming algorithm, one of the three basic paradigms of evolutionary computation (the other two are evolution strategies and genetic algorithms ; also see Bäck for an overview of evolutionary algorithms. Both authors convincingly argue that the goal-driven adaptation of behavior is achieved by Darwinian evolution, which can appropriately be emulated on a computer by evolutionary algorithms. Moreover, Fogel claims that "Evolution provides the solution to the problem of how to solve problems." (p. 259), provided that evolutionary computation relies on the careful observation and abstraction of the process of natural evolution.
Evolutionary programming (EP) has been successfully applied to many parameter optimization proble... more Evolutionary programming (EP) has been successfully applied to many parameter optimization problems. We propose a mean mutation operator, consisting of a linear combination of Gaussian and Cauchy mutations. Preliminary results indicate that both the adaptive and non-adaptive versions of the mean mutation operator are capable of producing solutions that are as good as, or better than those produced by Gaussian mutations alone. The success of the adaptive operator could be attributed to its ability to self-adapt the shape of the probability density function that generates the mutations during the run.
Controlling unstable nonlinear systems with neural networks can be problematic. Two examples pres... more Controlling unstable nonlinear systems with neural networks can be problematic. Two examples presented here show that evolutionary programming provides a feasible method for addressing such control problems.The successful application of classic control design techniques ...
3.3 Experimental Conditions 4.3 Varying Population Size In all experiments; 20,000 objective func... more 3.3 Experimental Conditions 4.3 Varying Population Size In all experiments; 20,000 objective function evaluations were made, regardless of the number of variables being searched-The experi ine rus were of the form (jj,43ll) or ()Х, 3u), ie, parents were selected at random to ...
IEEE Transactions on Evolutionary Computation, 1999
Complex adaptive systems have historically been studied using simplifications that mandate determ... more Complex adaptive systems have historically been studied using simplifications that mandate deterministic interactions between agents or instead treat their interactions only with regard to their statistical expectation. This has led to an anticipation, even in the case of agents employing inductive reasoning in light of limited information, that such systems may have equilibria that can be predicted a priori. This hypothesis is tested here using a simulation of a simple market economy in which each agent's behavior is based on the result of an iterative evolutionary process of variation and selection applied to competing internal models of its environment. The results indicate no tendency for convergence to stability or a longterm equilibrium and highlight fundamental differences between deterministic and stochastic models of complex adaptive systems.
Artificial neural networks are applied to the problem of detecting breast cancer from histologic ... more Artificial neural networks are applied to the problem of detecting breast cancer from histologic data. Evolutionary programming is used to train the networks. This stochastic optimization method reduces the chance of becoming trapped in locally optimal weight sets. Preliminary results indicate that very parsimonious neural nets can outperform other methods reported in the literature on the same data. The results are statistically significant.
Evolutionary algorithms, including evolutionary programming and evolution strategies, have often ... more Evolutionary algorithms, including evolutionary programming and evolution strategies, have often been applied to real-valued function optimization problems. These algorithms generally operate directly on the real values to be optimized, in contrast with genetic algorithms which usually operate on a separately coded transformation of the objective variables. Evolutionary algorithms often rely on a second-level optimization of strategy parameters, tunable variables that in part determine how each parent will generate offspring. Two alternative methods for performing this second-level optimization have been proposed and are compared across a series of function optimization tasks. The results appear to favor the approach offered originally in evolution strategies, although the applicability of the findings may be limited to the case where each parameter of a parent solution is perturbed independently of all others.
IEEE Transactions on Evolutionary Computation, 1997
Consideration is given to the effects of representations and operators in evolutionary algorithms... more Consideration is given to the effects of representations and operators in evolutionary algorithms. In particular, theorems are presented which establish, under some general assumptions, that no choice of cardinality of a representation offers any intrinsic advantage over another. Functionally equivalent algorithms can be constructed regardless of the chosen representation. Further, a similar effective equivalence of variation operators is shown such that no intrinsic advantage accrues to any particular one-parent operator or any particular two-parent operator.
A s the subtitle indicates, the book by David Fogel is not just a technical monograph about evolu... more A s the subtitle indicates, the book by David Fogel is not just a technical monograph about evolutionary computation, a subfield of computer science that deals with algorithms gleaned from the model of organic evolution, but also emphasizes a philosophical view of machine intelligence (artificial intelligence) and its relation to evolutionary processes. The definition of intelligence as "the capability of a system to adapt its behavior to meet its goals in a range of environments…" (p. 24) dates back to the '60s, when Lawrence J. Fogel (David's father) developed the evolutionary programming algorithm, one of the three basic paradigms of evolutionary computation (the other two are evolution strategies and genetic algorithms ; also see Bäck for an overview of evolutionary algorithms. Both authors convincingly argue that the goal-driven adaptation of behavior is achieved by Darwinian evolution, which can appropriately be emulated on a computer by evolutionary algorithms. Moreover, Fogel claims that "Evolution provides the solution to the problem of how to solve problems." (p. 259), provided that evolutionary computation relies on the careful observation and abstraction of the process of natural evolution.
Evolutionary programming (EP) has been successfully applied to many parameter optimization proble... more Evolutionary programming (EP) has been successfully applied to many parameter optimization problems. We propose a mean mutation operator, consisting of a linear combination of Gaussian and Cauchy mutations. Preliminary results indicate that both the adaptive and non-adaptive versions of the mean mutation operator are capable of producing solutions that are as good as, or better than those produced by Gaussian mutations alone. The success of the adaptive operator could be attributed to its ability to self-adapt the shape of the probability density function that generates the mutations during the run.
Controlling unstable nonlinear systems with neural networks can be problematic. Two examples pres... more Controlling unstable nonlinear systems with neural networks can be problematic. Two examples presented here show that evolutionary programming provides a feasible method for addressing such control problems.The successful application of classic control design techniques ...
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