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- Kengo Katayama
- 2001

- 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-based… (More)

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

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

In this paper, we propose an effective local search algorithm based on variable depth search (VDS) for the MCP. The VDS has been first successfully applied by Lin and Kernighan to the traveling salesman problem [5] and the graph partitioning problem [4]. Their algorithms are often called k-opt local search. The basic concept of the k-opt local search based… (More)

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

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>-opt… (More)

- Kengo Katayama, Hiroshi Yamashita, Hiroyuki Narihisa
- IEEE Congress on Evolutionary Computation
- 2007

We address a problem of finding an optimal node placement that minimizes the amount of traffics by reducing the weighted hop distances in multihop lightwave networks. The problem is called Node Placement Problem (NPP). NPP is known to be NP-hard and one of the most important problems in wavelength division multiplexing (WDM) based networks. In this paper we… (More)

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>primary… (More)

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

This paper presents a simple iterated local search metaheuristic 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 diversifies… (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 4change 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 of… (More)