Gomez, F. & Miikulainen, R. (1996). Incremental Evolution of Complex General Behavior. Technical Report AI96-248, Austin, TX: University of Texas.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....first with 6 neurons and the second with 4 neurons (xor 6 4 2) 3 Results 3.1 Performance Direct evolution failed to come up with a good solution for the generalized XOR problem (fitness of 0.2421 and 0. 2563 for xor 5 2 and xor 6 4 2, correspondingly ) therefore we used incremental evolution [9]. This is executed by having large pellets with radiuses of 0.1 (instaed of 0.08) and small pellets with a significantly reduced size in the initial stage of the evolution, and then gradually modifying the pellets sizes to their original values as the incremental evolution successfully progresses. ....

Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5 (1997) 317--342

....similar to h i but a few are signi cantly di erent. Delta coding is an e ective method for preventing premature convergence to a local maxima by restoring diversity to the population. It is particularly well suited to helping populations adjust to sudden changes in their training environment [5]. Hence, it is an excellent way to seed a new population from the results of an earlier layer. 2.3 Keepaway The experiments reported in this paper are all in a keepaway subtask of robotic soccer [18] In keepaway, one team of agents, the keepers, attempts to maintain possession of the ball while ....

F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342, 1997.

....first with 6 neurons and the second with 4 neurons (xor 6 4 2) 3 Results 3.1 Performance Direct evolution failed to come up with a good solution for the generalized XOR problem (fitness of 0.2421 and 0. 2563 for xor 5 2 and xor 6 4 2, correspondingly ) therefore we used incremental evolution [9]. This is executed by having large pellets with radiuses of 0.1 (instaed of 0.08) and small pellets with a significantly reduced size in the initial stage of the evolution, and then gradually modifying the pellets sizes to their original values as the incremental evolution successfully progresses. ....

Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5 (1997) 317--342

....behavioral problems. Although simple robotic behaviors have been developed using ER methods [4 6] it has not yet been shown that ER methods can be used to develop sophisticated behavioral robotic control systems. Various researchers have investigated methods such as incremental evolution [7][8] and minimal simulation [9] to overcome the problems associated with the fitness function formulation. These methods offer improvements to the ER methods; however, it has not been shown that they will lead to the development of advanced behavioral control systems. One potential method to ....

F. Gomez, R. Miikkulainen, Incremental Evolution of Complex General Behavior, Adaptive Behavior, Vol. 5, pp. 317342, 1997

....robot control research. ER focuses on the automatic design of model free robot controllers using evolutionary computing methods. Over the course of last decade, proof of concept research in the field of ER has been conducted. Much of this work was done using computer based simulations only [1][2] 6] Examples of ER research conducted with real robots include the evolution of walking behaviors in hexapod and octopod robots [7] 8] and the evolution of simple behavioral controllers for small mobile robots[9] 10] The later include the development of phototaxis behaviors [11] 12] and of ....

F. Gomez, R. Miikkulainen, Incremental Evolution of Complex General Behavior, Adaptive Behavior, Vol. 5, pp. 317-342, 1997.

....a sophisticated method based on incremental evolution for solving this problem. Incremental evolution does not evolve controller directly to do goal behavior in an environment, but starting with simpler environments gradually develops the controller with more general and complex environments [7, 8]. The controller composed of one module has a difficulty to make the robot to perform complex behavior. To overcome this shortcoming, some researchers combine several modules evolved or programmed to do a simple behavior such as going straight, avoiding obstacles, seeking object, and so on. ....

.... t, t2, t3 . tn are derived by transforming a goal task in incremental evolution, where n is the number of tasks and t, is the goal task. In this set, ti is easier task than ti for all i: O i n. Thus, population is evaluated in task t and then task t and it does in goal task, t, finally [7]. It is expected to produce complex and general behaviors which can adapt in changing environment. Figure 4 shows the incremental evolution of CAM Brain. In this process, task is changed into more difficult one and new population is created from successful individuals when satisfied controller for ....

F. Gomez and R. Miikkulainen, "Incremental evolution of complex general behavior," Adaptive Behavior, Vol. 5, pp 317-342, 1997.

....time intensive than learning, much as in the real world. 3. 1 SANE In the Symbiotic Adaptive Neuro Evolution algorithm, we deal with a population of individual neurons, each of which is represented by a numerical vector defining the weight of its connections to each of the input and output units [3, 4, 5, 6, 10]. In addition, there is a population of network blueprints consisting of pointers to neurons in the population. Individual networks are built out of the subset of the neurons specified by one of the blueprints, and the performance of the network as a whole is assigned to both the blueprint and to ....

....given neuron type, wherein such neurons breed with reasonably similar neurons often enough to produce useful results, can be difficult. The obvious solution is to segregate neurons on the basis of type. 3. 2 ESP The Enforced Sub Population variant of SANE is based on just this sort of separation [3, 6]. Rather than having one large pool of neurons with network blueprints, ESP maintains a separate population of neurons for each position in the network. Building a network then consists of selecting exactly one neuron from each of these sub populations. Since the populations are kept separate ....

Faustino Gomez & Risto Miikkulainen. Incremental Evolution of Complex General Behavior. Manuscript, 1996.

..... t n are derived by transforming a goal task in incremental evolution, where n is the number of tasks and t n is the goal task. In this set, t is easier task than t i 1 for all i: 0 n i # Thus, population is evaluated in task t and then task t i 1 and it does in goal task, t n , finally [8]. It is expected to produce complex and general behaviors which can adapt in changing environment. Figure 4 shows the procedure of the incremental evolution with CAM Brain. The robot controller is evolved incrementally by starting with simpler environments and gradually evolving the controller ....

F. Gomez and R. Miikkulainen, "Incremental evolution of complex general behavior," Adaptive Behavior, vol. 5, pp. 317-342, 1997.

....and network coding. We test two different environments, one of them with increasing difficulty. The latter serves to obtain an environment difficult enough to achieve a selective pressure toward higher eye numbers. Often it proves to be impossible to start at once with a very difficult task (cf. (Gomez Miikkulainen 1997)) Results and outlook are presented in Sec. 4. 2 Scenario For the simulations we used the simulation environment XRaptor (Mossinger et al. 1997) It provides a model for a 2 dimensional continuous world, in which the agents can move. The agent body contains only the sensory and motoric ....

Gomez, F., and Miikkulainen, R. 1997. Incremental evolution of complex general behavior. Adaptive Behavior 5(3-4):317--342.

....However, we also believe that there are cases where learning will fail to guide evolution to areas of increased tness. In other cases where the tness criterion have failed to provide credit to partial solutions, it has been found bene cial to resort to a strategy known as incremental evolution [4, 3, 9]. Rather than using a randomly generated initial population, incremental evolution tries to adapt a population to a goal task starting from a population that already has been adapted to a simpler but related task, known as an evaluation task. The idea here is that the search space of the ....

....that are good initial solutions to the goal task. In cases where a single evaluation task is insucient, a sequence of evaluation tasks may be used. A potential problem with this approach is that the population has to be suciently diverse to be able to adapt to a succeeding task in the sequence [3]. We believe that this diversity problem may be less severe in a combination of evolution and learning such as ours than when adaptation is performed by evolution only. The reason for this is the hypothesis that learning can maintain diversity in an evolving population [1] This is supposed to ....

F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5(3-4):317-342, 1997.

....Two discoveries made in prior work with SANE are particularly relevant for the natural deduction domain: incremental evolution and brainstorming. In the usual direct evolution, each network in the population is evaluated based on the full desired functionality in the task. In incremental evolution (Gomez and Miikkulainen 1997), the population is evolved to achieve a subset of the full desired functionality, and gradually more functionality is added. For example, in the direct approach, networks would be evolved both to find relevant rules and to come up with short proofs. In the incremental approach, networks would ....

Gomez, F. and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342.

....8 outputs) A similar trend can be observed in evolutionary robotics. In order to overcome this problem several researchers in the field of robotics have demonstrated that incremental evolution can be successfully applied to stochastic dynamic problems when implemented using neural networks [9], 10] 11] In incremental evolution, neural networks learn complex general behaviour by starting with simple behavior and incrementally making the task more general. We observe that while methods like the variable length chromosome [1] etc. are able too handle long chromosome strings, they ....

Gomez F. and Miikkulainen R. Incremental evolution of complex general behaviour. Adaptive Behaviour., 5:317--342, 1997.

....ANNs. 0 Work in this eld can be divided to the development of isolated ANNs, evolving to maximize a certain target function on one hand [Kitano, 1990, Harrald and Kamstra, 1997] and the development of embedded ANNs, serving as the control mechanism for an autonomous agent, on the other hand [Gomez and Miikkulainen, 1997, Jakobi, 1998, Scheier et al. 1998, Kodjabachian and Meyer, 1998] In the latter case the agents perform certain behavioural tasks, and their performance level in these tasks serves as the basis for evolutionary selection. This new paradigm of Evolved ANNs (EANNs) is clearly very interesting ....

....a limiting e ect on the scalability and speed of the evolutionary process. With the application of more sophisticated genetic encoding schemes, such as grammatical or ontogenic encodings [Kitano, 1990, Cangelosi et al. 1994] and of more ecient selection procedures such as incremental evolution [Gomez and Miikkulainen, 1997], one may expect the evolution of larger recurrent EANNs, processing more complex sensory input to achieve more intelligent behaviours. The similarity to known neural mechanisms that was achieved even under the current basic and almost toy like techniques leads us to believe that once better ....

Faustino Gomez and Risto Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5(3/4):317-342, 1997.

.... lack of a rigorous metric of agent behavior has been recognized [7] how do we ascertain and describe the sophistication of an agent Recently, information theoretic tools have been used to measure and adjust environmental complexity to facilitate agent learning in various domains, including PE [11, 13]; information theory has also come into use for the measurement of agent behavior, for example in a discrete state space [23] and a linear form of the PE game [10] Here, we will introduce the use of information theory to measure agent behavior and environmental complexity in a two dimensional, ....

F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

....randomly generated genotypes) In fact, one possible solution to this problem is the use of incremental evolution . In this case, we start with a simplified version of the task and, after we get individuals able to solve such a simple case, we progressively move to more and more complex cases [5, 10, 13]. This type of approach can overcome the bootstrap problem, although it also has the negative consequence of increasing the amount of supervision required and the risk of introducing inappropriate constraints. In the case of incremental evolution in fact, the experimenter should determine not only ....

Gomez, F., & Miikkulainem, R. (1997). Incremental Evolution of Complex General Behavior, Adaptive Behavior, 5, 317-342.

....difficult because none of the individuals in the initial generation might display competences which can be credited by the fitness function. Various approaches have been suggested, such as gradually increasing the complexity of the environment and or modifying the fitness function during evolution [7, 17, 13]. In some circumstances, changing the environment is equivalent to modifying the morphology of the robot. For example, instead of attempting to evolve from scratch a complex behavior based on vision and appropriate control of a gripper module, it can be more fruitful to proceed gradually from a ....

F. Gomez and R. Miikkulainem. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

....approach to the neural networks architecture. Neural nets work there with dynamic signals. The net is built with spiking neurons, and coappearance of spikes of the neurons is the main information representation within a net. The other interesting method is incremental learning, described by Gomez and Miikkulainen (1996) as an extension to reinforcement or supervised learning methods. In symbol processing, designers often use fuzzy sets, decision trees and others algorithms, such as A , or method called ID3 with its extension C4.5 (Quinlan 1993) based on Information Theory by Shannon. Incorporated together those ....

Gomez, F. and Miikkulainen, R. (1996). Incremental evolution of complex general behavior, To appear in Adaptive Behavior 5:317-342.

....randomly generated genotypes) In fact, one possible solution to this problem is the use of incremental evolution . In this case, we start with a simplified version of the task and, after we get individuals able to solve such a simple case, we progressively move to more and more complex cases [17, 18, 19]. This type of approach can overcome the bootstrap problem, although it also has the negative consequence of increasing the amount of supervision required and the risk of introducing inappropriate constraints. In the case of incremental evolution in fact, the experimenter should determine not only ....

Gomez, F., Miikkulainem, R.: Incremental Evolution of Complex General Behavior, Adaptive Behavior, 5 (1997) 317-342

....sequential decision tasks, the problem may be too complex to evolve the desired behavior all at once. Simple behaviors may evolve that give some fitness benefits, but may not be essential to the optimal solutions. Far worse, these behaviors may in fact be detrimental to the optimal solutions. Gomez and Miikkulainen (1996) refer to these behaviors as mechanical behaviors. Once mechanical behaviors emerge, it may be difficult to redirect the population towards the (often opposing) desired behaviors. An example of detrimental mechanical behaviors occurs in the game of Othello (section 6.1.2) A very simple strategy ....

....good overall winning strategies. The opponent s skill level was then increased, and the population adapted this strategy into the difficult to master mobility strategy. Similarly, incremental approaches may be necessary in other difficult tasks to avoid convergence on less desirable behaviors. Gomez and Miikkulainen (1996) explored the advantages of incremental evolution in several difficult problems, including enemy avoidance, catching a prey, and balancing multiple poles. They found that problems that could not be solved through direct evolution could often be solved using an incremental approach. 8.2.3 Online ....

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Gomez, F., and Miikkulainen, R. (1996). Incremental evolution of complex general behavior.

....the population adapted successfully to the harder task. Again his new population was recycled and used as the starting point for the even harder task of tracking a moving target. Working in this incremental fashion it should be possible to gradually build up quite complex behaviors (see also [14]) Also allowing the morphology of the robot to be developed along with the controller may simplify the task solution considerably. 3.4 Seeking and Avoiding Light [29] Carbot is a modified toy car which is approximately 15cm wide and 23cm long and is equipped with two light sensors and several ....

F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5(3--4):317--342, 1997.

....mappings that can make control greatly more accurate and robust, but, unfortunately, still require signi cant domain knowledge to train. In this paper, we propose a method for making the development of nless sounding rockets more economical by using Enforced SubPopulations (ESP; [5, 6]) to evolve a neural network guidance system. As a test case, we will focus on a nless version of the Interorbital Systems RSX 2 rocket ( gure 1) The RSX 2 is a liquid fueled sounding rocket that uses the di erential thrust of its four engines to control attitude. By evolving a neural network ....

....This score is then normalized and the best neurons within each subpopulation are mated to form new neurons. By evolving neurons in separate subpopulations, the specialized sub functions needed for good networks are evolved more eciently. 2 Enforced Subpopulations (ESP) Enforced Subpopulations [5, 6] is a neuroevolution method that extends the Symbiotic, Adaptive Neuroevolution algorithm (SANE; 7] ESP and SANE di er from other NE methods in that they evolve partial solutions or neurons instead of complete networks, and a subset of these neurons are put together to form a complete network. ....

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Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5 (1997) 317-342

....behaviors are learned gradually, beginning with easy tasks and advancing through successively more challenging ones. Gomez and Miikkulainen showed that this method can learn more e ective and more general behavior than direct evolution in several dynamic control tasks, including prey capture [2] and non Markovian double pole balancing [3] We apply incremental evolution to keepaway by changing the taker s speed. When evolution begins, the taker can move only 10 as quickly as the keepers. We evaluate each network in 20 games of keepaway and sum its scores (numbers of completed ....

F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342, 1997.

....with new structure. Thus, when a species in NEAT is on a local optimum, it is possible that by adding a new connection, a new dimension of freedom may open up, leading to a path away from the local optimum. A parallel can be drawn between structure evolution in NEAT and incremental evolution [5, 19]. Incremental evolution is a method used to train a system to solve harder tasks than it normally could by training it on incrementally more challenging tasks. The idea is that NE is likely to get stuck on a local optimum when attempting to solve the harder task directly. However, after solving ....

F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

....to how con dent they were of their outputs and how often they were selected. Although at rst it seems that such tness information might be too noisy, the situation is very similar to those of SANE and ESP neuroevolution methods described in [Moriarty and Miikkulainen, 1997] Moriarty, 1997] [Gomez and Miikkulainen, 1997], and [Gomez and Miikkulainen, 1999] where populations of neurons are evolved to form good neural networks. Each neuron receives a tness based on how well the whole neural network performed in the task: in e ect, the neurons are evolved to speciate into useful subtask that work well together. In ....

Gomez, F. and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5:317{ 342.

....for searching the space of neural network parameters. Instead of training a network by performing gradient descent on an error surface, the GA samples the space of networks and recombines those that perform best on the task in question. 3 Enforced Subpopulations Enforced Subpopulations (ESP; [5, 6]) is a neuroevolution method that extends the Symbiotic, Adaptive Neuroevolution algorithm (SANE; 8] to tasks that require memory. ESP and SANE di er from other NE methods in that they evolve partial solutions or neurons instead of complete networks, and a subset of these neurons are put ....

Gomez, F., and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342.

....is necessary for them to cooperate effectively in the task This paper explores these questions in the context of machine learning, where a team of neural networks is evolved using genetic algorithms to solve a cooperative task. The Enforced Subpopulations method of neuroevolution (ESP [7, 8]) which has proven highly efficient in single agent reinforcement learning tasks, is first extended to multi agent evolution. The method is then evaluated in a pursuit and evasion task where a team of several predators must cooperate to capture a fast moving prey. The main contribution is to ....

....are those in which the solution can be naturally modularized into subcomponents that interact or cooperate to solve the problem. Each subcomponent can then be evolved in its own population, and each population contributes its best individual to the solution. For example, Gomez and Miikkulainen [7] developed a method called Enforced Subpopulations (ESP) to evolve populations of neurons to form a neural network. A neuron was selected from each population to form the hidden layer units of a neural network, which was evaluated on the problem; the fitness was then passed back to the ....

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Gomez, F., and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342.

....starting with a population of minimal topologies is advantageous. Finally, performance comparisons suggest that the evolution of structure can be used to gain efficiency over the evolution of fixed topologies. A parallel can be drawn between structure evolution in NEAT and incremental evolution (Gomez and Miikkulainen 1997; Wieland 1991) Incremental evolution is a method used to train a system to solve harder tasks than it normally could by training it on incrementally more challenging tasks. NE is likely to get stuck on a local optimum when attempting to solve the harder task directly. However, after solving the ....

Gomez, F., and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342.

.... Miikkulainen, 1995; Pollack, Blair Land, 1996; Richards, Moriarty Miikkulainen, 1997) Also, evolution in environments related to gaming have been successful, such as in its application to stochastic, dynamic tasks like foraging, herding, communication, and the context of prey capture (Gomez and Miikkulainen, 1997; Nolfi, Elman, Parisi, 1994; Werner Dyer, 1990, 1993) In all of this previous work, populations are evolved off line. At each generation, individuals play a round of the game first, and are then evaluated. The next generation is created based on those individuals that did well over the course ....

Gomez, F., and Miikkulainen, R. (1997). Incremental Evolution of Complex General Behavior. Adaptive Behavior, 5:317-342.

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Gomez, F., and Miikkulainen, R. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-- 342, 1997.

....were tried; the performance was somewhat weaker below 8 hidden units, but very similar for larger hidden layer sizes. Such robustness is very useful in applying EuSANE to new applications. 4 Future Work Enforced Subpopulations (ESP) is another extension of SANE that is currently being developed (Gomez and Miikkulainen, 1997). It is based on enforcing speciation among the SANE neurons. First comparisons with ESP indicate that it is stronger on long runs needing many evaluations; EuSANE, however, has a clear advantage on shorter runs. This result suggests utilizing a restarting policy (Mossinger, 1995) with EuSANE: it ....

Gomez, F., and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5(3-4):317--342.

....diversity in the blueprint population, and second, it explores new structures created by the neuron population. 6 Applying SANE to Go SANE has previously been shown effective in several sequential decision tasks including robot control [7, 8, 9] constraint satisfaction [10] pursuit and evasion [3], and the game of Othello [6, 8, 10] This paper will evaluate the usefulness of SANE in learning to play go. SANE is used to evolve networks to play on small boards against a simple computer opponent, and the scale up properties are evaluated. In order to apply SANE, three aspects of the ....

Faustino Gomez and Risto Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

....It can now be solved so easily that it provides little or no insight about a system s ability. Neuroevolution (NE) systems (i.e. systems that evolve neural networks using genetic algorithms) for example, often find solutions in the initial random population [Moriarty and Miikkulainen, 1996; Gomez and Miikkulainen, 1997] In response to this need for a new benchmark, the basic pole balancing task has been extended in a variety of ways. Wieland[1991] presented several variations to the standard single pole task that can be grouped into two categories: 1) modifications to the mechanical system itself such as ....

....is a double pole configuration where two poles of unequal length must be balanced simultaneously. Even with complete state information this problem is very difficult requiring extremely precise control to solve. In this paper, we demonstrate a neuroevolution method, Enforced Sub populations (ESP;Gomez and Miikkulainen 1997), on an even harder version of this task in which the two poles must be balanced without velocity information. This task represents a significant leap in terms of difficulty. We show that ESP can solve this task, and can do so more efficiently than other methods have been able to solve it even ....

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Faustino Gomez and Risto Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

....basic pole balancing problem obsolete. It can now be solved so easily that it provides little or no insight about a system s ability. Neuroevolution (NE) systems (i.e. systems that evolve neural networks using genetic algorithms) for example, often find solutions in the initial random population [5, 6]. In response to this need for a new benchmark, a variety of ways to extend the basic pole balancing task have been suggested. Wieland [7] presented a series of increasingly difficult variations on the standard pole balancing task This research was supported in part by National Science ....

....Figure 1: The Enforced Sub Populations Method (ESP) The population of neurons is segregated into sub populations shown here as clusters of circles. The network is formed by randomly selecting one neuron from each subpopulation. culminating in a two pole version. In Gomez and Miikkulainen [5], the Enforced Sub Populations (ESP) method was shown to be significantly faster than other NE methods on this control problem. Here we present a different, more intuitive and realistic task, where the cart and pole can move in a plane instead of just a 1 D track. We demonstrate how ESP can evolve ....

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F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

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Gomez, F. & Miikulainen, R. (1996). Incremental Evolution of Complex General Behavior. Technical Report AI96-248, Austin, TX: University of Texas.

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Gomez, F. & Miikulainen, R. (1996). Incremental Evolution of Complex General Behavior. Technical Report AI96-248, Austin, TX: University of Texas.

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F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

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F. Gomez and R. Mikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342, 1997.

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F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

No context found.

Faustino Gomez and Risto Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

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F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317--342, 1997.

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Gomez, F. and Miikkulainen, R., "Incremental Evolution of Complex General Behavior", Adaptive Behavior, Vol. 5, No. 3/4, 317-342 (1997).

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F. Gomez, R. Miikkulainen, Incremental evolution of complex general behavior, Adaptive Behavior 5 (1997) 317--342.

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F. Gomez, R. Miikkulainen, Incremental evolution of complex general behavior, Adaptive Behavior 5 (1997) 317--342.

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Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5 (1997) 317--342

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