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- Peter Merz, Kengo Katayama
- Bio Systems
- 2004

This paper presents a memetic algorithm, a highly effective evolutionary algorithm incorporating local search for solving the unconstrained binary quadratic programming problem (BQP). To justify the approach, a fitness landscape analysis is conducted experimentally for several instances of the BQP. The results of the analysis show that recombination-basedâ€¦ (More)

- Kengo Katayama
- 2001

- Kengo Katayama, Hiroyuki Narihisa
- European Journal of Operational Research
- 2001

- Kengo Katayama, Akihiro Hamamoto, Hiroyuki Narihisa
- Inf. Process. Lett.
- 2005

We propose a variable depth search based algorithm, called k-opt local search (KLS), for the maximum clique problem. KLS efficiently explores the k-opt neighborhood defined as the set of neighbors that can be obtained by a sequence of several add and drop moves that are adaptively changed in the feasible search space. Computational results on DIMACSâ€¦ (More)

- Kengo Katayama, Hiroyuki Narihisa
- SAC
- 1999

This paper proposes a new iterared local search (ILS) algorit.hm ihar escapes from local optima usin,-a geuet ic crossover. In usual IL9 for solving the rraveling salesman problem, a double-bridge 4-change move is geuerally employed as a useful technique to escape from t.he local opt ima fouud by a local search procedure. Proposed ILS uses a technique ofâ€¦ (More)

- Kengo Katayama, Masafumi Tani, Hiroyuki Narihisa
- GECCO
- 2000

- Hiroyuki Narihisa, Keiko Kohmoto, Takahiro Taniguchi, Mayumi Ohta, Kengo Katayama
- IEEE Congress on Evolutionary Computation
- 2006

This paper presents a novel model of reinforcement learning agents. A feature of our learning agent model is to integrate analytic hierarchy process (AHP) into a standard reinforcement learning agent model, which consists of three modules: state recognition, learning, and action selecting modules. In our model, AHP module is designed with <i>primaryâ€¦ (More)

- Kengo Katayama, Masashi Sadamatsu, Hiroyuki Narihisa
- EvoCOP
- 2007

This paper presents a simple iterated local search meta-heuristic incorporating a k-opt local search (KLS), called Iterated KLS (IKLS for short), for solving the maximum clique problem (MCP). IKLS consists of three components: LocalSearch at which KLS is used, a Kick called LEC-Kick that escapes from local optima, and Restart that occasionally diversifiesâ€¦ (More)

This paper presents a local search algorithm based on variable depth search, called the <i>k-opt local search</i>, for the maximum clique problem. The <i>k-opt</i> local search performs add and drop moves, each of which can be interpreted as 1-opt move, to search a <i>k-opt</i> neighborhood solution at each iteration until no better <i>k</i>-optâ€¦ (More)