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- http://evolutioninmaterio.com/ - using FPGAs in intrinsic evolution, e.g. the device is actually programmed and tested.
- - Adrian Thompson's homepage. There are many PDFs of his work on his homepage.
- Parallel genetic algorithms on programmable graphics hardware
- basically deals with optimizing mutation and fitness evaluation using the parallel arcitecture of a GPU: larger populations can be evaluated at one time.
- does not concern the intrinsic evolution of algorithms to the GPU, as in the Adrian's work.
- uses a linear conguent generator to produce random numbers.
- used a really simple problem: Colville minimization problem which need only search through a four-dimensional space.
- Cellular genetic algoritms and local search for 3-SAT problem on Graphic Hardware
- concerning SAT: satisfiabillity technique: " many practical problems, such as graph coloring, job-shop scheduling, and real-world scheduling can be represented as a SAT problem.
- SAT-3 refers to the length of the search clause. length 3 is apparently very hard..
- they use a combination of greedy search (flip the bit that increases the fitness the largest ammount) and random-walk via point mutations to keep the algorithm away from local minima.
- also use cellular genetic algorithm which works better on a GPU): select the optimal neignbor, not global, individual.
- only used a GeForce 6200 gpu, but it was still 5x faster than a p4 2.4ghz.
- Evolution of a robot controller using cartesian genetic programming
- cartesian programming has many advantages over traditional tree based methods - e.g. blot-free evolution & faster evolution through neutral search.
- cartesian programming is characterized by its encoding of a graph as a string of integers that represent the functions and connections between graph nodes, and program inputs and outputs.
- this encoding was developed in the course of evolving electronic circuits, e.g. above ?
- can encode a non-connected graph. the genetic material that is not utilized is analogous to biological junk DNA.
- even in converged populations, small mutations can produce large changes in phenotypic behavior.
- in this work he only uses directed graphs - there are no cycles & an organized flow of information.
- mentions automatically defined functions - what is this??
- used diffusion to define the fitness values of particular locations in the map. the fewer particles there eventually were in a grid location, the higher the fitness value of the robot that managed to get there.
- Hardware evolution: on the nature of artifically evolved circuits - doctoral dissertation.
- because evolved circuits utilize the parasitic properties of devices, they have little tolerance of the value of components. Reverse engineering of the circuits evolved to improve tolerance is not easy.
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