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Book Categories |
| 1 | Adaptive business intelligence based on evolution strategies : some application examples of self-adaptive software | 1 |
| 2 | Extending the boundaries of design optimization by integrating fast optimization techniques with machine code based, linear genetic programming | 11 |
| 3 | Evolutionary optimization of approximating triangulations for surface reconstruction from unstructured 3D data | 31 |
| 4 | An evolutionary algorithm based on morphological associative memories for endmember selection in hyperspectral images | 45 |
| 5 | On a gradient-based evolution strategy for parametric illumination correction | 61 |
| 6 | A new chromosome codification for scheduling problems | 73 |
| 7 | Evolution-based learning of ontological knowledge for a large-scale multi-agent simulation | 83 |
| 8 | An evolutionary algorithms approach to phylogenetic tree construction | 99 |
| 9 | Robot controller evolution with macroevolutionary algorithms | 117 |
| 10 | Evolving natural language grammars | 129 |
| 11 | Evaluating protein structure prediction models with evolutionary algorithms | 143 |
| 12 | Learning decision rules by means of hybrid-encoded evolutionary algorithms | 159 |
| 13 | Evolvable hardware techniques for gate-level synthesis of combinational circuits | 177 |
| 14 | The evolutionary learning rule in system identification | 195 |
| 15 | Current and future research trends in evolutionary multiobjective optimization | 213 |
| 16 | Genetic algorithms with limited convergence | 233 |
| 17 | Evolution with sampled fitness functions | 255 |
| 18 | Molecular computing by signaling pathways | 269 |
| 19 | Strategy-oriented evolutionary games : toward a grammatical model of games | 285 |
| 20 | Discrete multiphase particle swarm optimization | 305 |
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
| WonderClub |
Robert Pafford
reviewed Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations on December 20, 2009
Information Processing with Evolutionary Algorithms: From Industrial Applications to Academic Speculations
I did not feel like this book provided any good information, and it felt padded to boot. In fact, after a while I lost patience and started skimming.
For instance, several pages were wasted on an explanation of an algorithm that could have been given in a single paragraph. Then time was spent on a very awkward proof involving the relationship between sums of SPECIFIC numbers, which one could simply test directly. The author may have made some point about the awkwardness of the proof, but there was no point to this. This was doubly damning because it undermined the author's argument that testing couldn't cover general correctness; so why waste time on a bad proof of a specific case that could be tested directly?
The rest of the book was this awkward. Discussions about comments in steps of proofs were given, yet no significant ties to program development were produced. When the author finally got to Hoare triples, I didn't understand why the previous pages were even written as none of the material could even be seen as a support to this.
The book is very short, even with all the padding; much of the book's length is dedicated to answers to the exercises. This leads me to doubt that there was enough material for a book. Overly long examples, pointless proofs and material that doesn't build up are the mark of a lack of substance. This material probably could have been covered in a single article.
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